Setup¶
Retrieve "GL-DPPD-7111_Mmus_Brain_CellType_GeneMarkers.csv" from this GitHub link and save it in the same directory as this notebook. The GitHub path is nasa/GeneLab_Data_Processing/scRNAseq/10X_Chromium_3prime_Data/GeneLab_CellType_GeneMarkers/GL-DPPD-7111_GeneMarker_Files.
Possible Map My Cells region keys = ["RHP", "RSP", "ACA", "PL-ILA-ORB", "AUD-TEa-PERI-ECT", "SS-GU-VISC", "MO-FRP", "PAL", "sAMY", "CTXsp", "HY", "STRv", "OLF", "LSX", "AI", "STRd", "VIS-PTLp", "VIS", "TH", "MOp", "ENT", "HIP", "P", "MB", "MY", "CB", "AUD", "SSp", "TEa-PERI-ECT"]
Use Conda
While in the folder containing this notebook:
conda env create -f rapidsc.yml
Navigate back to where you want to clone the scflow repository (I recommend home)
cd
Clone scflow from GitHub.
git clone git@github.com:easlinger/scflow.git
Navigate to the folder where scflow is:
pip install .
pip install senepy
For NVIDIA Drivers (Linux)
sudo apt update
sudo apt install -y build-essential dkms
sudo apt install -y wget
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"
sudo apt update
sudo apt install -y cuda
Imports & Display¶
%load_ext autoreload
%autoreload 2
%xmode plain
import os
import re
import logging
import warnings
import json
try:
import torch
torch.set_float32_matmul_precision("medium")
except Exception:
pass
# try:
# import rapids_singlecell as rsc
# except Exception:
# rsc = None
import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
from pandas.errors import PerformanceWarning
import pandas as pd
import numpy as np
import scflow
pd.set_option("display.max_rows", 500) # or None for unlimited rows
pd.set_option("display.max_columns", 100)
pd.set_option("display.width", 200)
class CategoricalFilter(logging.Filter):
def filter(self, record):
msg = record.getMessage()
# suppress "storing ... as categorical" logs
if "storing" in msg and "as categorical" in msg:
return False
# suppress DataFrame fragmentation warnings
if "DataFrame is highly fragmented" in msg:
return False
return True
# Apply to root logger (or your logger of choice)
logging.getLogger().addFilter(CategoricalFilter())
warnings.simplefilter("ignore", PerformanceWarning)
logger = logging.getLogger("anndata")
Exception reporting mode: Plain
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/jaxopt/__init__.py:59: DeprecationWarning: JAXopt is no longer maintained. See https://docs.jax.dev/en/latest/ for alternatives.
warnings.warn(
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/__init__.py:17: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
__version_info__ = tuple(LooseVersion(__version__).version)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/fields.py:198: RemovedInMarshmallow4Warning: Passing field metadata as a keyword arg is deprecated. Use the explicit `metadata=...` argument instead.
warnings.warn(
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pkg_resources/__init__.py:3146: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.
Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
declare_namespace(pkg)
Set Options (ACTIVELY SET THESE!)¶
# Processors to Use
n_processors = os.cpu_count() - 1 # how many processors to use
# If You Want Results Emailed
cur_file = os.path.join(os.path.abspath(""), "create_object_612.ipynb")
html_out = os.path.splitext(cur_file)[0] + ".html"
email = "elizabeth.aslinger@aya.yale.edu"
# set email to None to skip
# Set Data Sources & Species
species = "Mouse"
batches = ["OSD-612"] # in list even if just 1, or list multiple to integrate
# Set Source Data Directory & Output Options
superdirec = "/home/easlinger/data" # directory with original data
direcs = [os.path.join(superdirec, i) for i in batches]
# new h5ad will write to "data" sub-directory of where this notebook is
overwrite = True # allow overwrite of files?
file_concat = os.path.join("data", f"{'_'.join(batches)}_concatenated.h5ad")
file_new = os.path.join("data", f"{'_'.join(batches)}_integrated.h5ad")
# Set Sample & Batch IDs, Plus Other Potential Sources of Batch Effects
col_group = "Group" # age &/or space flight
col_age = "Characteristics[Age at Euthanasia]"
# col_age = "Factor Value[Age]"
col_condition = "Factor Value[Spaceflight]"
col_sample = "sample"
col_batch = "batch" if len(batches) > 1 else col_group # group=batch if 1
# covariates_categorical = ["Comment[Euthanasia Date]"]
# covariates_continuous = ["Time"]
covariates_categorical = None
covariates_continuous = None
# Do Sub-Clustering?
# subcluster_biggest = 1 # sub-cluster biggest cluster
# subcluster_biggest = 3 # sub-cluster biggest 3 clusters
subcluster_biggest = False # no sub-clustering
kws_cluster = dict(n_comps=50) # cluster individual samples
# kws_cluster = None # do not cluster individual samples
# vars_regress_out = ["Time", "pct_counts_mt",
# "total_counts"] # to regress out of concatenated object
vars_regress_out = None
# Set Annotation Sources
map_my_cells_source = "WMB-10X" if species == "Mouse" else "WHB-10X" if (
species == "Human") else None # Map My Cells atlas source
# map_my_cells_region_keys = None
map_my_cells_region_keys = [
"RSP", "ACA", "PL-ILA-ORB", "AUD-TEa-PERI-ECT", "SS-GU-VISC", "MO-FRP",
"AI", "VIS-PTLp", "VIS", "MOp", "AUD", "SSp",
"TEa-PERI-ECT"] # regional subset for Map My Cells
map_my_cells_cell_keys = ["Isocortex"] # pattern match: feature name column
if species == "Mouse":
model_celltypist = "Mouse_Whole_Brain.pkl"
else:
raise ValueError("Manually set CellTypist model for non-mouse!")
source_patterns = ["Brain", "Cortical", "cortex"] # for ToppGene
# Make Pre-Defined Marker Dictionary
cts_superhierarchical = {
"Neuron": ["Excitatory", "Inhibitory", "Glutamatergic", "GABAergic",
"Dopaminergic", "Serotonergic", "Cholinergic"]
} # if classified as Neuron + other, just keep more specific type(s)
rename_marker_based_annotation = {
"Excitatory | Inhibitory": "Excitatory-Inhibitory",
"Inhibitory | Inhibitory": "Excitatory-Inhibitory"
}
mks_a_priori = pd.read_csv("GL-DPPD-7111_Mmus_Brain_CellType_GeneMarkers.csv")
mks_a_priori.loc[:, "cellName"] = mks_a_priori.cellName.replace({
"Oligodendrocyte precursor cell": "OPC"}).apply(
lambda x: " ".join([i.capitalize() for i in re.sub(
" cell", "", x).split(" ")])).replace({
"Opc": "OPC"}) # capitalize cell names
mks_a_priori = mks_a_priori.set_index("cellName")["geneSymbol"]
mks_a_priori = dict(mks_a_priori.apply(lambda x: set(x.split(","))))
mks_a_priori.update({
"Neuroepithelial": {"Nes", "Notch1", "Sox2", "Sox10", "Hes1", "Hes3"},
"Excitatory": {"Slc17a7", "Slc30a3", "Tcf4",
"Slc17a6", "Slc6a1", "Baiap3",
"Grin1", "Grin2b", "Gls"},
"Inhibitory": {"Gad1", "Slc6a1", "Gabbr1", "Gabbr2",
"Gad2", "Slc32a1", "Oprm1", "Htr2c"},
# "Glutamatergic": {"Slc17a7", "Slc17a6", "Grin1", "Grin2b", "Gls"},
# "GABAergic": {"Slc6a1", "Gabbr1", "Gabbr2", "Gad2", "Gad1"},
# "Dopaminergic": {"Th", "Dat", "Foxa2", "Girk2", "Nurr1", "Lmx1b"},
# "Serotonergic": {"Tph", "Sert", "Pet1"},
# "Cholinergic": {"ChAT", "VAChT", "Acetylcholinesterase"}
})
markers_predefined = {**mks_a_priori}
if cts_superhierarchical is not None:
for i in cts_superhierarchical:
for k in [u for u in cts_superhierarchical[
i] if u in markers_predefined]:
markers_predefined[k] = markers_predefined[k].union(
markers_predefined[i])
_ = [markers_predefined.pop(i) for i in cts_superhierarchical]
mks_collapsed = {**mks_a_priori}
mks_collapsed["Neuron"] = mks_collapsed["Neuron"].union(mks_collapsed[
"Excitatory"]).union(mks_collapsed["Inhibitory"])
_ = mks_collapsed.pop("Excitatory")
_ = mks_collapsed.pop("Inhibitory")
Load Individual Sample Data¶
%%time
# Create a Subdirectory of Working Directory for Data Outputs
os.makedirs("data", exist_ok=True)
# Get Metadata
metadata = [pd.read_csv(os.path.join(
superdirec, f"{i}_metadata_{i}-ISA/s_{i}.txt"), sep=None,
engine="python").set_index("Source Name").rename_axis(
col_sample) for i in batches] # list of metadata
for u in np.arange(len(metadata)): # add a combined age & condition variable
metadata[u] = metadata[u].join(metadata[u].apply(
lambda x: x[col_condition] + str(
" | " + str(x[col_age]) + " Weeks" if (
col_age in metadata[u].columns and len(
metadata[u][col_age].unique()) > 1) else ""),
axis=1).to_frame(col_group)) # add space flight (x age if applicable)
# Load Data
adatas, files = {}, {}
for u, d in enumerate(direcs): # iterate directories, then samples within
for x in [i for i in os.listdir(d) if os.path.isdir(os.path.join(d, i))]:
ddd = [os.path.join(d, x, i) for i in os.listdir(os.path.join(d, x))]
if len(ddd) > 1: # ensure 1 subdirectory (for the sample) in folder
raise ValueError(f"More than one file: {ddd}")
sample = os.path.basename(os.path.join(d, x, ddd[0])) # sample ID
files[sample] = os.path.join(os.path.join(d, x, ddd[0]),
"filtered_feature_bc_matrix.h5") # file
adatas[sample] = sc.read_10x_h5(files[sample]) # read anndata (h5)
if col_batch: # if a batch column specified
adatas[sample].obs.loc[:, col_batch] = batches[u] # batch => .obs
adatas[sample].obs.loc[:, col_sample] = sample # sample ID => .obs
samp_metadata = metadata[u].loc[sample] if (
sample in metadata[u].index.values) else metadata[u].set_index(
"Sample Name").loc[sample] # extract sample-specific metadata
for v in samp_metadata.index.values: # loop metadata => .obs columns
adatas[sample].obs.loc[:, v] = samp_metadata.loc[v]
adatas[sample].obs.loc[:, f"n_cells_original_{col_sample}"] = adatas[
sample].obs.shape[0] # original number of cells
print(files)
metadata
{'RR10_BRN_GC_WT_G5': '/home/easlinger/data/OSD-612/RR10_BRN_GC_WT_G5-20250307T234232Z-001/RR10_BRN_GC_WT_G5/filtered_feature_bc_matrix.h5', 'RR10_BRN_FLT_WT_F1': '/home/easlinger/data/OSD-612/RR10_BRN_FLT_WT_F1-20250307T231611Z-001/RR10_BRN_FLT_WT_F1/filtered_feature_bc_matrix.h5', 'RR10_BRN_GC_WT_G1': '/home/easlinger/data/OSD-612/RR10_BRN_GC_WT_G1-20250307T210200Z-001/RR10_BRN_GC_WT_G1/filtered_feature_bc_matrix.h5', 'RR10_BRN_GC_WT_G9': '/home/easlinger/data/OSD-612/RR10_BRN_GC_WT_G9-20250307T234621Z-001/RR10_BRN_GC_WT_G9/filtered_feature_bc_matrix.h5', 'RR10_BRN_FLT_WT_F3': '/home/easlinger/data/OSD-612/RR10_BRN_FLT_WT_F3-20250307T205407Z-001/RR10_BRN_FLT_WT_F3/filtered_feature_bc_matrix.h5', 'RR10_BRN_GC_WT_G3': '/home/easlinger/data/OSD-612/RR10_BRN_GC_WT_G3-20250307T235702Z-001/RR10_BRN_GC_WT_G3/filtered_feature_bc_matrix.h5', 'RR10_BRN_FLT_WT_F7': '/home/easlinger/data/OSD-612/RR10_BRN_FLT_WT_F7-20250307T235407Z-001/RR10_BRN_FLT_WT_F7/filtered_feature_bc_matrix.h5', 'RR10_BRN_FLT_WT_F9': '/home/easlinger/data/OSD-612/RR10_BRN_FLT_WT_F9-20250307T210417Z-001/RR10_BRN_FLT_WT_F9/filtered_feature_bc_matrix.h5', 'RR10_BRN_FLT_WT_F5': '/home/easlinger/data/OSD-612/RR10_BRN_FLT_WT_F5-20250307T235113Z-001/RR10_BRN_FLT_WT_F5/filtered_feature_bc_matrix.h5', 'RR10_BRN_GC_WT_G7': '/home/easlinger/data/OSD-612/RR10_BRN_GC_WT_G7-20250308T000029Z-001/RR10_BRN_GC_WT_G7/filtered_feature_bc_matrix.h5'}
CPU times: user 6.25 s, sys: 713 ms, total: 6.96 s
Wall time: 7.01 s
[ Sample Name Characteristics[Organism] Term Source REF Term Accession Number Characteristics[Strain] Term Source REF.1 Term Accession Number.1 \
sample
RR-10_FL-01 RR10_BRN_FLT_WT_F1 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_FL-03 RR10_BRN_FLT_WT_F3 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_FL-05 RR10_BRN_FLT_WT_F5 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_FL-07 RR10_BRN_FLT_WT_F7 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_FL-09 RR10_BRN_FLT_WT_F9 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_GC-01 RR10_BRN_GC_WT_G1 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_GC-03 RR10_BRN_GC_WT_G3 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_GC-05 RR10_BRN_GC_WT_G5 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_GC-07 RR10_BRN_GC_WT_G7 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
RR-10_GC-09 RR10_BRN_GC_WT_G9 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... B6129SF2/J OSD https://osdr.nasa.gov/
Characteristics[Animal Source] Characteristics[Genotype] Term Source REF.2 Term Accession Number.2 Characteristics[Sex] Term Source REF.3 \
sample
RR-10_FL-01 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_FL-03 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_FL-05 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_FL-07 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_FL-09 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_GC-01 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_GC-03 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_GC-05 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_GC-07 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
RR-10_GC-09 Jackson Laboratory Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Female MESH
Term Accession Number.3 Characteristics[Material Type] Term Source REF.4 Term Accession Number.4 Factor Value[Spaceflight] Term Source REF.5 \
sample
RR-10_FL-01 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Space Flight MESH
RR-10_FL-03 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Space Flight MESH
RR-10_FL-05 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Space Flight MESH
RR-10_FL-07 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Space Flight MESH
RR-10_FL-09 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Space Flight MESH
RR-10_GC-01 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Ground Control OSD
RR-10_GC-03 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Ground Control OSD
RR-10_GC-05 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Ground Control OSD
RR-10_GC-07 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Ground Control OSD
RR-10_GC-09 http://purl.bioontology.org/ontology/MESH/D005260 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Ground Control OSD
Term Accession Number.5 Characteristics[Age at Launch] Unit Term Source REF.6 Term Accession Number.6 Characteristics[Age at Euthanasia] \
sample
RR-10_FL-01 http://purl.bioontology.org/ontology/MESH/D013026 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_FL-03 http://purl.bioontology.org/ontology/MESH/D013026 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_FL-05 http://purl.bioontology.org/ontology/MESH/D013026 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_FL-07 http://purl.bioontology.org/ontology/MESH/D013026 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_FL-09 http://purl.bioontology.org/ontology/MESH/D013026 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_GC-01 https://osdr.nasa.gov/ 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_GC-03 https://osdr.nasa.gov/ 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_GC-05 https://osdr.nasa.gov/ 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_GC-07 https://osdr.nasa.gov/ 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
RR-10_GC-09 https://osdr.nasa.gov/ 14 - 15 week UO http://purl.obolibrary.org/obo/UO_0000034 18 -19
Unit.1 Term Source REF.7 Term Accession Number.7 Protocol REF Parameter Value[habitat] Parameter Value[duration] Unit.2 \
sample
RR-10_FL-01 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_FL-03 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_FL-05 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_FL-07 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_FL-09 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_GC-01 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_GC-03 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_GC-05 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_GC-07 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
RR-10_GC-09 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) 28 day
Term Source REF.8 Term Accession Number.8 Parameter Value[Enrichment material] Parameter Value[light cycle] \
sample
RR-10_FL-01 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_FL-03 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_FL-05 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_FL-07 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_FL-09 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_GC-01 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_GC-03 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_GC-05 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_GC-07 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
RR-10_GC-09 UO http://purl.obolibrary.org/obo/UO_0000033 Cocoons 12 h light/dark cycle, lights on at 7:00 GMT
Parameter Value[Diet] Parameter Value[Feeding Schedule] Parameter Value[Euthanasia Method] Parameter Value[Carcass Preservation Method] \
sample
RR-10_FL-01 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_FL-03 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_FL-05 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_FL-07 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_FL-09 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_GC-01 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_GC-03 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_GC-05 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_GC-07 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
RR-10_GC-09 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum Bilateral thoracotomy with sedation, Ketamine/... Cryochiller
Parameter Value[Body Weight at Euthanasia] Unit.3 Term Source REF.9 Term Accession Number.9 Protocol REF.1 Parameter Value[Sample Preservation Method] \
sample
RR-10_FL-01 NaN Not Available OSD https://osdr.nasa.gov/ sample collection Liquid Nitrogen
RR-10_FL-03 NaN Not Available OSD https://osdr.nasa.gov/ sample collection Liquid Nitrogen
RR-10_FL-05 NaN Not Available OSD https://osdr.nasa.gov/ sample collection Liquid Nitrogen
RR-10_FL-07 NaN Not Available OSD https://osdr.nasa.gov/ sample collection Liquid Nitrogen
RR-10_FL-09 NaN Not Available OSD https://osdr.nasa.gov/ sample collection Liquid Nitrogen
RR-10_GC-01 25.61 gram UO http://purl.obolibrary.org/obo/UO_0000021 sample collection Liquid Nitrogen
RR-10_GC-03 23.66 gram UO http://purl.obolibrary.org/obo/UO_0000021 sample collection Liquid Nitrogen
RR-10_GC-05 23.68 gram UO http://purl.obolibrary.org/obo/UO_0000021 sample collection Liquid Nitrogen
RR-10_GC-07 21.28 gram UO http://purl.obolibrary.org/obo/UO_0000021 sample collection Liquid Nitrogen
RR-10_GC-09 22.81 gram UO http://purl.obolibrary.org/obo/UO_0000021 sample collection Liquid Nitrogen
Term Source REF.10 Term Accession Number.10 Parameter Value[Sample Storage Temperature] Unit.4 Term Source REF.11 Term Accession Number.11 \
sample
RR-10_FL-01 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_FL-03 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_FL-05 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_FL-07 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_FL-09 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_GC-01 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_GC-03 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_GC-05 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_GC-07 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
RR-10_GC-09 NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027
Comment[RFID] Comment[ALSDA Subject ID] Comment[Euthanasia Date] Comment[Euthanasia Time (hh:mm:ss)] Comment[BSP Dissection Date] Comment[Source Description] \
sample
RR-10_FL-01 6E3B7A3143 799 02-Jan-2021 09:34:00 GMT 12-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_FL-03 6E3D711909 799 02-Jan-2021 10:35:00 GMT 12-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_FL-05 6E3D657F3D 799 02-Jan-2021 11:18:00 GMT 12-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_FL-07 6E3D710131 799 02-Jan-2021 13:26:00 GMT 12-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_FL-09 6E36781618 799 02-Jan-2021 14:09:00 GMT 12-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_GC-01 6E3B32156C 800 05-Jan-2021 04:34:00 EST 14-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_GC-03 6E37564D01 800 05-Jan-2021 05:34:00 EST 14-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_GC-05 6E3C500B24 800 05-Jan-2021 06:18:00 EST 14-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_GC-07 6E37071254 800 05-Jan-2021 08:26:00 EST 14-May-2021 Frozen carcass dissected on Earth post-flight
RR-10_GC-09 6E3D552B5B 800 05-Jan-2021 09:10:00 EST 14-May-2021 Frozen carcass dissected on Earth post-flight
Comment[Partial Body Weight on BSP Dissection Date] Group
sample
RR-10_FL-01 18.98 gram Space Flight
RR-10_FL-03 19.32 gram Space Flight
RR-10_FL-05 19.98 gram Space Flight
RR-10_FL-07 19.87 gram Space Flight
RR-10_FL-09 21.45 gram Space Flight
RR-10_GC-01 Not Available Ground Control
RR-10_GC-03 Not Available Ground Control
RR-10_GC-05 Not Available Ground Control
RR-10_GC-07 Not Available Ground Control
RR-10_GC-09 Not Available Ground Control ]
QC¶
Perform Sample-Specific QC¶
%matplotlib inline
plot_qc = False # change to True to get sample-level QC plots (a bit slow)
qcs, n_cells_by_counts, descriptives, figs = scflow.pp.perform_qc_multi(
adatas, col_batch=col_batch, col_sample=col_sample, plot=plot_qc,
percentiles=[0.025, 0.10, 0.25, 0.50, 0.75, 0.85, 0.90, 0.975],
figsize=(10, 10)) # perform QC on individual samples
for x in qcs: # iterate QC metrics % plot percentiles by group
fig = sns.catplot(qcs, y=x, hue=col_batch, kind="violin")
fig.fig.suptitle(x)
fig = sns.catplot(descriptives.loc[:, :, x][[
i for i in descriptives if ("%" in i)]].stack().to_frame("Value"),
x="Metric", y="Value", kind="bar",
hue=col_batch, height=10)
fig.fig.suptitle(x)
descriptives.stack().unstack("Variable").round()
| Variable | n_cells_by_counts | n_genes_by_counts | pct_counts_mt | total_counts | ||
|---|---|---|---|---|---|---|
| sample | Group | Metric | ||||
| RR10_BRN_FLT_WT_F1 | Space Flight | count | 56748.0 | 11879.0 | 11879.0 | 11879.0 |
| mean | 398.0 | 1900.0 | 1.0 | 4298.0 | ||
| std | 1096.0 | 1096.0 | 1.0 | 3711.0 | ||
| min | 0.0 | 44.0 | 0.0 | 48.0 | ||
| 2.5% | 0.0 | 395.0 | 0.0 | 523.0 | ||
| 10% | 0.0 | 540.0 | 0.0 | 761.0 | ||
| 25% | 0.0 | 892.0 | 0.0 | 1387.0 | ||
| 50% | 4.0 | 1917.0 | 0.0 | 3680.0 | ||
| 75% | 153.0 | 2593.0 | 1.0 | 5865.0 | ||
| 85% | 625.0 | 3003.0 | 1.0 | 7406.0 | ||
| 90% | 1195.0 | 3306.0 | 2.0 | 8687.0 | ||
| 97.5% | 3985.0 | 4238.0 | 5.0 | 13230.0 | ||
| max | 11876.0 | 11082.0 | 25.0 | 93231.0 | ||
| RR10_BRN_FLT_WT_F3 | Space Flight | count | 56748.0 | 12875.0 | 12875.0 | 12875.0 |
| mean | 384.0 | 1692.0 | 4.0 | 3583.0 | ||
| std | 1086.0 | 986.0 | 5.0 | 3186.0 | ||
| min | 0.0 | 9.0 | 0.0 | 9.0 | ||
| 2.5% | 0.0 | 362.0 | 0.0 | 488.0 | ||
| 10% | 0.0 | 532.0 | 0.0 | 783.0 | ||
| 25% | 0.0 | 966.0 | 1.0 | 1546.0 | ||
| 50% | 4.0 | 1576.0 | 2.0 | 2850.0 | ||
| 75% | 144.0 | 2158.0 | 4.0 | 4414.0 | ||
| 85% | 600.0 | 2629.0 | 7.0 | 5865.0 | ||
| 90% | 1128.0 | 2976.0 | 10.0 | 7074.0 | ||
| 97.5% | 3794.0 | 4068.0 | 20.0 | 12019.0 | ||
| max | 12866.0 | 9150.0 | 47.0 | 46452.0 | ||
| RR10_BRN_FLT_WT_F5 | Space Flight | count | 56748.0 | 3307.0 | 3307.0 | 3307.0 |
| mean | 94.0 | 1620.0 | 2.0 | 3400.0 | ||
| std | 264.0 | 960.0 | 2.0 | 2795.0 | ||
| min | 0.0 | 64.0 | 0.0 | 98.0 | ||
| 2.5% | 0.0 | 313.0 | 0.0 | 423.0 | ||
| 10% | 0.0 | 429.0 | 0.0 | 601.0 | ||
| 25% | 0.0 | 678.0 | 0.0 | 1017.0 | ||
| 50% | 1.0 | 1694.0 | 1.0 | 3107.0 | ||
| 75% | 36.0 | 2235.0 | 2.0 | 4674.0 | ||
| 85% | 149.0 | 2581.0 | 3.0 | 5749.0 | ||
| 90% | 282.0 | 2835.0 | 3.0 | 6681.0 | ||
| 97.5% | 923.0 | 3718.0 | 7.0 | 10531.0 | ||
| max | 3306.0 | 5592.0 | 46.0 | 22030.0 | ||
| RR10_BRN_FLT_WT_F7 | Space Flight | count | 56748.0 | 5075.0 | 5075.0 | 5075.0 |
| mean | 125.0 | 1401.0 | 2.0 | 2880.0 | ||
| std | 358.0 | 988.0 | 4.0 | 3042.0 | ||
| min | 0.0 | 17.0 | 0.0 | 17.0 | ||
| 2.5% | 0.0 | 211.0 | 0.0 | 265.0 | ||
| 10% | 0.0 | 388.0 | 0.0 | 520.0 | ||
| 25% | 0.0 | 548.0 | 0.0 | 798.0 | ||
| 50% | 1.0 | 1294.0 | 1.0 | 2140.0 | ||
| 75% | 47.0 | 1972.0 | 2.0 | 3890.0 | ||
| 85% | 193.0 | 2332.0 | 4.0 | 4921.0 | ||
| 90% | 366.0 | 2640.0 | 6.0 | 6102.0 | ||
| 97.5% | 1224.0 | 3747.0 | 14.0 | 10972.0 | ||
| max | 5049.0 | 10616.0 | 41.0 | 75161.0 | ||
| RR10_BRN_FLT_WT_F9 | Space Flight | count | 56748.0 | 3562.0 | 3562.0 | 3562.0 |
| mean | 103.0 | 1641.0 | 3.0 | 3590.0 | ||
| std | 294.0 | 915.0 | 4.0 | 2933.0 | ||
| min | 0.0 | 52.0 | 0.0 | 76.0 | ||
| 2.5% | 0.0 | 360.0 | 0.0 | 499.0 | ||
| 10% | 0.0 | 494.0 | 0.0 | 725.0 | ||
| 25% | 0.0 | 814.0 | 1.0 | 1315.0 | ||
| 50% | 1.0 | 1669.0 | 1.0 | 3163.0 | ||
| 75% | 38.0 | 2190.0 | 3.0 | 4717.0 | ||
| 85% | 156.0 | 2489.0 | 5.0 | 5791.0 | ||
| 90% | 295.0 | 2757.0 | 6.0 | 6753.0 | ||
| 97.5% | 1030.0 | 3788.0 | 14.0 | 11845.0 | ||
| max | 3559.0 | 5328.0 | 46.0 | 23456.0 | ||
| RR10_BRN_GC_WT_G1 | Ground Control | count | 56748.0 | 7937.0 | 7937.0 | 7937.0 |
| mean | 241.0 | 1721.0 | 2.0 | 3588.0 | ||
| std | 674.0 | 1005.0 | 3.0 | 3127.0 | ||
| min | 0.0 | 18.0 | 0.0 | 24.0 | ||
| 2.5% | 0.0 | 389.0 | 0.0 | 513.0 | ||
| 10% | 0.0 | 528.0 | 0.0 | 750.0 | ||
| 25% | 0.0 | 860.0 | 0.0 | 1330.0 | ||
| 50% | 3.0 | 1668.0 | 1.0 | 2988.0 | ||
| 75% | 93.0 | 2277.0 | 2.0 | 4711.0 | ||
| 85% | 376.0 | 2679.0 | 3.0 | 6023.0 | ||
| 90% | 710.0 | 2980.0 | 5.0 | 7061.0 | ||
| 97.5% | 2367.0 | 4031.0 | 11.0 | 11378.0 | ||
| max | 7933.0 | 11210.0 | 38.0 | 65469.0 | ||
| RR10_BRN_GC_WT_G3 | Ground Control | count | 56748.0 | 15280.0 | 15280.0 | 15280.0 |
| mean | 378.0 | 1402.0 | 3.0 | 2801.0 | ||
| std | 1129.0 | 810.0 | 3.0 | 2375.0 | ||
| min | 0.0 | 14.0 | 0.0 | 16.0 | ||
| 2.5% | 0.0 | 292.0 | 0.0 | 387.0 | ||
| 10% | 0.0 | 422.0 | 0.0 | 597.0 | ||
| 25% | 0.0 | 778.0 | 1.0 | 1195.0 | ||
| 50% | 4.0 | 1343.0 | 1.0 | 2359.0 | ||
| 75% | 134.0 | 1800.0 | 3.0 | 3530.0 | ||
| 85% | 551.0 | 2162.0 | 4.0 | 4530.0 | ||
| 90% | 1046.0 | 2458.0 | 6.0 | 5488.0 | ||
| 97.5% | 3708.0 | 3313.0 | 13.0 | 8888.0 | ||
| max | 15274.0 | 9847.0 | 41.0 | 50964.0 | ||
| RR10_BRN_GC_WT_G5 | Ground Control | count | 56748.0 | 4231.0 | 4231.0 | 4231.0 |
| mean | 123.0 | 1651.0 | 2.0 | 3511.0 | ||
| std | 351.0 | 914.0 | 3.0 | 2765.0 | ||
| min | 0.0 | 72.0 | 0.0 | 87.0 | ||
| 2.5% | 0.0 | 336.0 | 0.0 | 451.0 | ||
| 10% | 0.0 | 477.0 | 0.0 | 693.0 | ||
| 25% | 0.0 | 818.0 | 0.0 | 1272.0 | ||
| 50% | 1.0 | 1678.0 | 1.0 | 3106.0 | ||
| 75% | 45.0 | 2254.0 | 2.0 | 4864.0 | ||
| 85% | 187.0 | 2590.0 | 4.0 | 5986.0 | ||
| 90% | 356.0 | 2837.0 | 5.0 | 6887.0 | ||
| 97.5% | 1222.0 | 3541.0 | 12.0 | 10013.0 | ||
| max | 4228.0 | 8837.0 | 50.0 | 48219.0 | ||
| RR10_BRN_GC_WT_G7 | Ground Control | count | 56748.0 | 3926.0 | 3926.0 | 3926.0 |
| mean | 110.0 | 1596.0 | 2.0 | 3352.0 | ||
| std | 316.0 | 852.0 | 4.0 | 2616.0 | ||
| min | 0.0 | 37.0 | 0.0 | 39.0 | ||
| 2.5% | 0.0 | 331.0 | 0.0 | 438.0 | ||
| 10% | 0.0 | 483.0 | 0.0 | 703.0 | ||
| 25% | 0.0 | 884.0 | 0.0 | 1422.0 | ||
| 50% | 1.0 | 1642.0 | 1.0 | 3086.0 | ||
| 75% | 43.0 | 2086.0 | 2.0 | 4410.0 | ||
| 85% | 169.0 | 2345.0 | 4.0 | 5198.0 | ||
| 90% | 318.0 | 2578.0 | 5.0 | 5919.0 | ||
| 97.5% | 1080.0 | 3486.0 | 14.0 | 9604.0 | ||
| max | 3922.0 | 7450.0 | 63.0 | 34684.0 | ||
| RR10_BRN_GC_WT_G9 | Ground Control | count | 56748.0 | 8496.0 | 8496.0 | 8496.0 |
| mean | 181.0 | 1209.0 | 3.0 | 2292.0 | ||
| std | 551.0 | 783.0 | 4.0 | 2060.0 | ||
| min | 0.0 | 9.0 | 0.0 | 9.0 | ||
| 2.5% | 0.0 | 239.0 | 0.0 | 300.0 | ||
| 10% | 0.0 | 324.0 | 0.0 | 431.0 | ||
| 25% | 0.0 | 510.0 | 0.0 | 719.0 | ||
| 50% | 2.0 | 1164.0 | 1.0 | 1898.0 | ||
| 75% | 61.0 | 1679.0 | 3.0 | 3140.0 | ||
| 85% | 261.0 | 1977.0 | 5.0 | 3956.0 | ||
| 90% | 497.0 | 2216.0 | 7.0 | 4646.0 | ||
| 97.5% | 1766.0 | 2981.0 | 15.0 | 7469.0 | ||
| max | 8489.0 | 8067.0 | 57.0 | 35718.0 |
Auto-Detect Filtering Thresholds¶
Use 2.5th and/& 97.5th percentile (sample-specific) as minimum genes per cell and minimum and maximum total counts (subject to specified absolute minima). Use 97.5th percentile as upper bound for percent mitochondrial count.
Use an absolute minimum cells per gene.
Also include arguments to run a PCA on individual samples before integrating.
To preview the effect of setting different absolute minima/maxima, use code such as the following to see how much data may be dropped:
descriptives.stack().unstack("Variable")["n_genes_by_counts"].loc[:, :, "85%", :]
descriptives.stack().unstack("Variable")["pct_mt"].loc[:, :, "85%", :]
descriptives.stack().unstack("Variable")["total_counts"].loc[:, :, "90%", :]
descriptives.stack().unstack("Variable")["total_counts"].loc[:, :, "10%", :]
descriptives.stack().unstack("Variable")["n_cells_by_counts"].loc[:, :, "10%", :]
# Options
bounds = descriptives[["2.5%", "97.5%"]].apply(lambda x: list(
x), axis=1).unstack("Variable") # list top/bottom 5% (~sample, variable)
abs_min_cells = 3 # regardless of %ile, minimum cells to retain gene
abs_min_genes = 200 # regardless of %ile, minimum genes to retain cell
abs_min_count = 300 # regardless of %ile, minimum counts to retain cell
abs_max_mt = 5 # regardless of %ile, absolute maximum mitochonrial content
n_top_genes = 2000 # number of top genes to count as HVGs
# Set Thresholds
kws_pp = {}
for x in adatas:
b_x = bounds.loc[x]
b_counts = b_x["total_counts"] if isinstance(b_x[
"total_counts"], list) else b_x["total_counts"].iloc[0]
b_counts = [max(b_counts[0], abs_min_count), b_counts[1]]
kws_pp[x] = {
"min_max_genes": [round(i) if i else i for i in [max((b_x[
"n_genes_by_counts"] if isinstance(b_x[
"n_genes_by_counts"], list) else b_x[
"n_genes_by_counts"].iloc[0])[0], abs_min_genes), None]],
"min_max_cells": [round(abs_min_cells), None],
# "min_max_cells": [max((b_x["n_cells_by_counts"] if isinstance(
# b_x["n_genes_by_counts"], list) else b_x[
# "n_genes_by_counts"].iloc[0])[0], abs_min_cells), None],
# "max_mt": min(abs_max_mt, (b_x["pct_counts_mt"] if isinstance(b_x[
# "pct_counts_mt"], list) else b_x["pct_counts_mt"].iloc[0])[1]),
"max_mt": abs_max_mt,
"min_max_counts": [round(i) if i else i for i in b_counts],
# "vars_regress_out": ["total_counts"],
"target_sum": 1e4,
"zero_center": True, "max_value": 10, # scaling
"n_top_genes": n_top_genes,
"doublet_detection": "drop"
}
print("\n".join([f"{s}: {kws_pp[s]}" for s in kws_pp]))
pd.DataFrame(kws_pp).T
RR10_BRN_GC_WT_G5: {'min_max_genes': [336, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [451, 10013], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_FLT_WT_F1: {'min_max_genes': [395, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [523, 13230], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_GC_WT_G1: {'min_max_genes': [389, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [513, 11378], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_GC_WT_G9: {'min_max_genes': [239, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [300, 7469], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_FLT_WT_F3: {'min_max_genes': [362, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [488, 12019], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_GC_WT_G3: {'min_max_genes': [292, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [387, 8888], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_FLT_WT_F7: {'min_max_genes': [211, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [300, 10972], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_FLT_WT_F9: {'min_max_genes': [360, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [499, 11845], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_FLT_WT_F5: {'min_max_genes': [313, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [423, 10531], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RR10_BRN_GC_WT_G7: {'min_max_genes': [331, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [438, 9604], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
| min_max_genes | min_max_cells | max_mt | min_max_counts | target_sum | zero_center | max_value | n_top_genes | doublet_detection | |
|---|---|---|---|---|---|---|---|---|---|
| RR10_BRN_GC_WT_G5 | [336, None] | [3, None] | 5 | [451, 10013] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_FLT_WT_F1 | [395, None] | [3, None] | 5 | [523, 13230] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_GC_WT_G1 | [389, None] | [3, None] | 5 | [513, 11378] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_GC_WT_G9 | [239, None] | [3, None] | 5 | [300, 7469] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_FLT_WT_F3 | [362, None] | [3, None] | 5 | [488, 12019] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_GC_WT_G3 | [292, None] | [3, None] | 5 | [387, 8888] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_FLT_WT_F7 | [211, None] | [3, None] | 5 | [300, 10972] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_FLT_WT_F9 | [360, None] | [3, None] | 5 | [499, 11845] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_FLT_WT_F5 | [313, None] | [3, None] | 5 | [423, 10531] | 10000.0 | True | 10 | 2000 | drop |
| RR10_BRN_GC_WT_G7 | [331, None] | [3, None] | 5 | [438, 9604] | 10000.0 | True | 10 | 2000 | drop |
Integrate¶
In-Memory Approach¶
See the "On-Disk Approach" for a more sophisticated approach to defining kws_integrate (more options).
# %%time
# # Integrate
# join_method = "outer" # or "inner"
# kws_integrate = {"kws_pp": kws_pp, "n_top_genes": 10000,
# "fill_value": np.nan,
# "col_batch": None, # suppress using batch as covariate
# "join": join_method, "merge": "unique", "use_rapids": True}
# self = scflow.Rna(adatas, col_sample=col_sample, col_batch=col_batch,
# kws_integrate=kws_integrate)
# self.rna.obs = self.rna.obs.assign(kws_integrate=str(kws_integrate))
# # Write Files for Processed/Integrated Objects?
# if overwrite is True or not os.path.exists(file_new):
# os.makedirs("data", exist_ok=True)
# self.rna.write_h5ad(file_new)
# for x in adatas:
# pfp = os.path.join("data", f"{x}_processed.h5ad")
# if overwrite is True or not os.path.exists(pfp):
# adatas[x].write_h5ad(pfp)
# del adatas # to save memory; now integrated in `self.rna`
# # Display
# print(self.rna)
# # print(self.rna.var.head())
# self.rna.obs
On-Disk Approach¶
Preprocess Individual¶
Filter & Normalize¶
# Preprocess
if overwrite is False and os.path.exists(pfp):
raise ValueError("Must be able to overwrite to use on-disk option")
files_individual = dict(zip(files.keys(), [os.path.join(
"data", f"{x}_processed.h5ad") for x in files])) # new individual files
var_names = [] # to store genes not filtered out for each sample
for x in files: # iterate sample files
print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}")
adatas[x].obs.loc[:, f"kws_pp_{col_sample}"] = str(kws_pp[x]) # store kws
adatas[x] = scflow.pp.preprocess(
adatas[x], **kws_pp[x], plot_qc=False) # preprocess data
var_names += [set(adatas[x].var_names)] # track what genes still present
# Decide Join Method
shared_genes = set.intersection(*var_names) # genes in all after filtering
all_genes = set.union(*var_names) # genes in any post-filter sample
print(f"{len(shared_genes)} genes present in all samples post-filtering (out"
f" of {len(all_genes)} total genes present in any post-filter sample)")
join_method = "inner" if len(shared_genes) / len(all_genes) >= 0.5 else \
"outer" # outer join if <1/2 of genes shared across all samples
# Show Number of Cells Pre- & Post-Filtering
n_cells = pd.concat([pd.Series([
adatas[x].obs.shape[0] for x in adatas], index=adatas).to_frame(
"N Cells"), pd.Series([adatas[x].obs["n_cells_original_sample"].iloc[
0] for x in adatas], index=adatas).to_frame(
"N Cells Original")], axis=1)
n_cells = n_cells.assign(Percent_Dropped=100 * (1 - n_cells[
"N Cells"] / n_cells["N Cells Original"]))
n_cells
================================================================================
RR10_BRN_GC_WT_G5
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 4231 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 12 cells that have less than 336 genes expressed
***Filtering genes by cells...
filtered out 31450 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.41
Detected doublet rate = 0.4%
Estimated detectable doublet fraction = 12.3%
Overall doublet rate:
Expected = 5.0%
Estimated = 3.4%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_FLT_WT_F1
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 11879 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 37 cells that have less than 395 genes expressed
***Filtering genes by cells...
filtered out 26568 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.12
Detected doublet rate = 10.1%
Estimated detectable doublet fraction = 65.8%
Overall doublet rate:
Expected = 5.0%
Estimated = 15.4%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_GC_WT_G1
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 7937 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 15 cells that have less than 389 genes expressed
***Filtering genes by cells...
filtered out 28263 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.49
Detected doublet rate = 0.0%
Estimated detectable doublet fraction = 0.2%
Overall doublet rate:
Expected = 5.0%
Estimated = 8.7%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_GC_WT_G9
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 8496 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 26 cells that have less than 239 genes expressed
***Filtering genes by cells...
filtered out 30148 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.49
Detected doublet rate = 0.0%
Estimated detectable doublet fraction = 0.7%
Overall doublet rate:
Expected = 5.0%
Estimated = 6.5%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_FLT_WT_F3
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 12875 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 35 cells that have less than 362 genes expressed
***Filtering genes by cells...
filtered out 26867 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.46
Detected doublet rate = 0.0%
Estimated detectable doublet fraction = 0.1%
Overall doublet rate:
Expected = 5.0%
Estimated = 0.0%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_GC_WT_G3
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 15280 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 32 cells that have less than 292 genes expressed
***Filtering genes by cells...
filtered out 26677 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.45
Detected doublet rate = 0.0%
Estimated detectable doublet fraction = 0.0%
Overall doublet rate:
Expected = 5.0%
Estimated = 44.4%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_FLT_WT_F7
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 5075 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 1 cells that have less than 211 genes expressed
***Filtering genes by cells...
filtered out 31379 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.14
Detected doublet rate = 6.8%
Estimated detectable doublet fraction = 55.3%
Overall doublet rate:
Expected = 5.0%
Estimated = 12.3%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_FLT_WT_F9
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 3562 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 9 cells that have less than 360 genes expressed
***Filtering genes by cells...
filtered out 31890 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.38
Detected doublet rate = 0.4%
Estimated detectable doublet fraction = 9.7%
Overall doublet rate:
Expected = 5.0%
Estimated = 4.2%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_FLT_WT_F5
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 3307 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 7 cells that have less than 313 genes expressed
***Filtering genes by cells...
filtered out 32229 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.18
Detected doublet rate = 4.6%
Estimated detectable doublet fraction = 60.8%
Overall doublet rate:
Expected = 5.0%
Estimated = 7.5%
***Normalizing...
***Detecting highly variable genes...
***Scaling data...
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 3926 × 56748
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
layers: 'counts'
***Filtering cells by counts...
***Filtering cells by genes...
filtered out 8 cells that have less than 331 genes expressed
***Filtering genes by cells...
filtered out 31180 genes that are detected in less than 3 cells
***Filtering cells by mitochondrial gene content...
***Performing doublet detection...
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) ret = func(*args, **kwargs)
Automatically set threshold at doublet score = 0.14 Detected doublet rate = 7.8% Estimated detectable doublet fraction = 65.3% Overall doublet rate: Expected = 5.0% Estimated = 12.0% ***Normalizing... ***Detecting highly variable genes... ***Scaling data... 21692 genes present in all samples post-filtering (out of 33273 total genes present in any post-filter sample)
| N Cells | N Cells Original | Percent_Dropped | |
|---|---|---|---|
| RR10_BRN_GC_WT_G5 | 3609 | 4231 | 14.701016 |
| RR10_BRN_FLT_WT_F1 | 9930 | 11879 | 16.407105 |
| RR10_BRN_GC_WT_G1 | 6826 | 7937 | 13.997732 |
| RR10_BRN_GC_WT_G9 | 6823 | 8496 | 19.691620 |
| RR10_BRN_FLT_WT_F3 | 9550 | 12875 | 25.825243 |
| RR10_BRN_GC_WT_G3 | 12739 | 15280 | 16.629581 |
| RR10_BRN_FLT_WT_F7 | 3994 | 5075 | 21.300493 |
| RR10_BRN_FLT_WT_F9 | 2924 | 3562 | 17.911286 |
| RR10_BRN_FLT_WT_F5 | 2833 | 3307 | 14.333233 |
| RR10_BRN_GC_WT_G7 | 3098 | 3926 | 21.090168 |
Test Preprocessing¶
Unit tests
for p, ann in zip([kws_pp[x] for x in kws_pp], [adatas[x] for x in adatas]):
print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}\n")
if join_method == "inner":
assert all(ann.var["n_cells_by_counts"] >= p["min_max_cells"][0])
assert all(ann.var["n_cells_by_counts"] <= p[
x]["min_max_cells"][1]) if kws_pp[x]["min_max_cells"][1] else True
assert all(ann.obs["n_genes_by_counts"] >= p["min_max_genes"][0])
assert all(ann.obs["n_genes_by_counts"] <= p[
"min_max_genes"][1]) if p["min_max_genes"][1] else True
assert all(ann.obs["pct_counts_mt"] <= p["max_mt"])
assert all(ann.obs["total_counts"] >= p["min_max_counts"][0])
assert all(ann.obs["total_counts"] <= p["min_max_counts"][1]) if (
p["min_max_counts"][1]) else True
print(p)
print(ann.obs[["n_genes", "pct_counts_mt", "total_counts"]
].describe().loc[["min", "max"]])
print(ann.var[["n_cells_by_counts"]].describe().loc[[
"min", "max"]])
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [336, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [451, 10013], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 337.0 0.0 455.0
max 3924.0 5.0 10012.0
n_cells_by_counts
min 3.0
max 4228.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [395, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [523, 13230], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 395.0 0.000000 523.0
max 4749.0 4.987212 13230.0
n_cells_by_counts
min 3.0
max 11876.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [389, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [513, 11378], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 389.0 0.0 513.0
max 4637.0 5.0 11375.0
n_cells_by_counts
min 3.0
max 7933.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [239, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [300, 7469], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 239.0 0.0 300.0
max 3721.0 5.0 7469.0
n_cells_by_counts
min 3.0
max 8489.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [362, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [488, 12019], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 362.0 0.000000 488.0
max 4813.0 4.997128 12018.0
n_cells_by_counts
min 3.0
max 12866.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [292, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [387, 8888], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 292.0 0.0 387.0
max 3948.0 5.0 8888.0
n_cells_by_counts
min 3.0
max 15274.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [211, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [300, 10972], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 230.0 0.000000 301.0
max 4462.0 4.992658 10957.0
n_cells_by_counts
min 3.0
max 5049.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [360, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [499, 11845], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 360.0 0.0 502.0
max 4342.0 5.0 11845.0
n_cells_by_counts
min 3.0
max 3559.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [313, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [423, 10531], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 314.0 0.000000 425.0
max 4113.0 4.979253 10529.0
n_cells_by_counts
min 3.0
max 3306.0
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
{'min_max_genes': [331, None], 'min_max_cells': [3, None], 'max_mt': 5, 'min_max_counts': [438, 9604], 'target_sum': 10000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
n_genes pct_counts_mt total_counts
min 331.0 0.000000 438.0
max 3620.0 4.991394 9531.0
n_cells_by_counts
min 3.0
max 3922.0
Cluster Individual¶
A Priori Clustering Parameters¶
# %%time
# # Clustering Parameter Options (Only Applied If `kws_cluster` != None)
# resolution_individual, min_dist_individual = 0.15, 1.5
# col_cluster_individual = "annotation_by_markers_individual"
# unlabeled_cat = "Heterogeneous" # if can't find one best-fit cell label
# cci_scanvi = col_cluster_individual + "_heterogeneous_collapsed"
# sep = " | " # separator for heterogeneous annotations
# # Preprocessing & (Optionally) Clustering
# if overwrite is False and os.path.exists(pfp):
# raise ValueError("Must be able to overwrite to use on-disk option")
# # del adatas
# for x in files: # iterate sample files
# if kws_cluster is not None: # cluster individual sample?
# kws_cl = {"resolution": resolution_individual[x],
# "min_dist": min_dist_individual[x], **kws_cluster}
# self.rna.obs.loc[:, "kws_cluster_individual"] = str(kws_cl)
# adatas[x] = scflow.pp.cluster(
# adatas[x], resolution=resolution_individual,
# min_dist=min_dist_individual, plot=False,
# col_celltype="leiden_individual", **kws_cl) # cluster
# sc.tl.rank_genes_groups(
# adatas[x], "leiden_individual", n_genes=None, rankby_abs=False,
# key_added="rank_genes_groups_leiden_individual",
# copy=False) # find markers/DEGs
# if markers_predefined is not None: # markers specified?
# _, adatas[x] = scflow.pp.annotate_by_marker_overlap(
# adatas[x], markers_predefined,
# col_celltype="leiden_individual",
# col_celltype_new=col_cluster_individual, sep=sep,
# celltypes_superhierarchical=cts_superhierarchical,
# # top_n_markers=20,
# adj_pval_threshold=1e-10,
# method="overlap_coef", inplace=True) # annotate by markers
# if rename_marker_based_annotation is not None:
# adatas[x].obs.loc[:, col_cluster_individual] = adatas[
# x].obs[col_cluster_individual].replace(
# rename_marker_based_annotation) # re-name
# adatas[x].obs.loc[:, cci_scanvi] = adatas[x].obs[
# col_cluster_individual].apply(lambda x: unlabeled_cat if (
# sep in x) else x) # re-label heterogeneous annotations
# var_names += [set(adatas[x].var_names)] # track what genes still there
# # Write Objects
# for x in adatas:
# print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}\n\n{adatas[x]}\n")
# adatas[x].write_h5ad(files_individual[x]) # write individual file
# # Plot
# if kws_cluster is not None:
# for x in files: # iterate sample files
# ccs = [v for v in ["leiden_individual", col_cluster_individual
# ] if v in adatas[x].obs] # columns for UMAP
# print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}")
# sc.pl.umap(adatas[x], color=ccs, wspace=0.4) # plot UMAP
# del adatas # save memory
Optimize Hyperparameters¶
Iterate different clustering parameters to ensure extraction of common cell types
Make sample-specific alterations to one sample's annotation
# Marker Dictionary for Annotation + Required & Prohibited Cell Types
use_mks = {**markers_predefined}
# use_mks = {**mks_collapsed}
cts_nonneuron = ["OPC", "Oligodendrocyte", "Astrocyte", "Microglial"]
req_cts = list(["Neuron"] if "Neuron" in use_mks else [
"Excitatory", "Inhibitory"] ) + cts_nonneuron # required cell types
prohib_cts = ["Excitatory-Inhibitory"] # prohibited cell types
# Clustering Parameters
resn_list = [0.2, 0.5, 0.1, 0.075, 0.15, 0.3, 0.25, 0.4]
dist_list = [1.5, 0.75, 0.5, 1, 0.3, 0.4]
# Column Names & Heterogeneous Cell Type Key/Separator
col_cluster_individual = "annotation_by_markers_individual"
unlabeled_cat = "Heterogeneous" # if can't find one best-fit cell label
cci_scanvi = col_cluster_individual + "_heterogeneous_collapsed"
sep = " | " # separator for heterogeneous annotations
# Clustering
for x in files: # iterate sample files
valid_cts = False
print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}")
for r in resn_list:
if valid_cts is True:
break
for t in dist_list:
if valid_cts is True:
break
c_i = f"leiden_individual_res{r}dist{t}"
cai = f"{col_cluster_individual}_res{r}dist{t}"
kws_cl = {"resolution": r, "min_dist": t, **kws_cluster}
adatas[x] = scflow.pp.cluster(
adatas[x], plot=False, col_celltype=c_i, **kws_cl)
sc.tl.rank_genes_groups(
adatas[x], c_i, n_genes=None,
key_added=f"rank_genes_groups_{c_i}",
copy=False) # find markers/DEGs
_, adatas[x] = scflow.pp.annotate_by_marker_overlap(
adatas[x], use_mks,
col_celltype=c_i, col_celltype_new=cai, sep=sep,
celltypes_superhierarchical=cts_superhierarchical,
# top_n_markers=20,
adj_pval_threshold=1e-15,
method="overlap_coef", inplace=True) # annotate
if rename_marker_based_annotation is not None:
adatas[x].obs.loc[:, cai] = adatas[x].obs[cai].replace(
rename_marker_based_annotation) # re-name
print(list(adatas[x].obs[cai].unique()))
adatas[x].obs.loc[:, cci_scanvi] = adatas[x].obs[
cai].apply(lambda x: unlabeled_cat if (sep in x) else x)
valid_cts = all([q in adatas[x].obs[cai].unique()
for q in req_cts])
valid_cts = valid_cts and (all([q not in adatas[x].obs[
cai].unique() for q in prohib_cts]))
valid_cts = False if any(adatas[x].obs[
cci_scanvi] == unlabeled_cat) else valid_cts
if valid_cts is True:
adatas[x].obs.loc[:, "leiden_individual"] = adatas[x].obs[c_i]
adatas[x].obs.loc[:, col_cluster_individual] = adatas[
x].obs[cai]
adatas[x].obs.loc[:, "kws_cluster_individual"] = str(kws_cl)
print(kws_cl)
else:
adatas[x].obs = adatas[x].obs.drop([i for i in [
cai, c_i, cci_scanvi] if i in adatas[x].obs], axis=1)
# Detect Samples with No Valid Clustering Scheme
no_valid = []
for x in files:
if all((i in adatas[x].obs for i in [
"leiden_individual", col_cluster_individual])) is False:
print(f"No valid clustering found for {x}")
no_valid += [x]
print(f"No Valid Clustering Scheme Yet: {no_valid}")
# # Individual Tweak
# samp = "RRRM2_BRN_GC_ISS-T_YNG_GY4"
# cols_tries = [i for i in adatas[samp].obs if "leiden_" in i]
# cts_try = cols_tries[14]
# _, adatas[samp] = scflow.pp.annotate_by_marker_overlap(
# adatas[samp], mks_collapsed, col_celltype=cts_try,
# col_celltype_new=col_cluster_individual + "_new", sep=sep,
# adj_pval_threshold=1e-5,
# method="overlap_coef", inplace=True) # annotate by markers
# adatas[samp].obs.loc[:, "leiden_individual"] = adatas[samp].obs[cts_try]
# adatas[samp].obs.loc[:, col_cluster_individual] = adatas[samp].obs[
# col_cluster_individual + "_new"]
# adatas[samp].obs["kws_cluster_individual"] = str({"resolution": float(
# cts_try.split("res")[1].split("dist")[0]), "min_dist": float(
# cts_try.split("res")[1].split("dist")[1])})
# Write Objects
for x in adatas:
print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}\n\n{adatas[x]}\n")
adatas[x].write_h5ad(files_individual[x]) # write individual file
# Print Parameters Used
for x in adatas:
print(adatas[x].obs["kws_cluster_individual"])
# Plot
for x in adatas:
sc.pl.pca_variance_ratio(adatas[x], log=True)
sc.pl.umap(adatas[x], color=[
"leiden_individual", col_cluster_individual], wspace=0.4)
del adatas
================================================================================ RR10_BRN_GC_WT_G5 ================================================================================ ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1.5... ***Performing Leiden clustering with resolution 0.2...
2025-09-01 16:43:07 | [INFO] init ... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Excitatory', 'Excitatory-Inhibitory', 'Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.75...
***Performing Leiden clustering with resolution 0.2...
['Excitatory', 'Excitatory-Inhibitory', 'Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.5...
***Performing Leiden clustering with resolution 0.2...
['Excitatory', 'Excitatory-Inhibitory', 'Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1...
***Performing Leiden clustering with resolution 0.2...
['Excitatory', 'Excitatory-Inhibitory', 'Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.3...
***Performing Leiden clustering with resolution 0.2...
['Excitatory', 'Excitatory-Inhibitory', 'Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.4...
***Performing Leiden clustering with resolution 0.2...
['Excitatory', 'Excitatory-Inhibitory', 'Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.5...
['Excitatory', 'Inhibitory', 'Excitatory-Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.75...
***Performing Leiden clustering with resolution 0.5...
['Excitatory', 'Inhibitory', 'Excitatory-Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.5...
***Performing Leiden clustering with resolution 0.5...
['Excitatory', 'Inhibitory', 'Excitatory-Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1...
***Performing Leiden clustering with resolution 0.5...
['Excitatory', 'Inhibitory', 'Excitatory-Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.3...
***Performing Leiden clustering with resolution 0.5...
['Excitatory', 'Inhibitory', 'Excitatory-Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.4...
***Performing Leiden clustering with resolution 0.5...
['Excitatory', 'Inhibitory', 'Excitatory-Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Pericyte', 'Microglial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.1...
['Excitatory', 'Inhibitory', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Microglial']
{'resolution': 0.1, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_FLT_WT_F1
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Oligodendrocyte', 'Excitatory-Inhibitory', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.75...
***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Excitatory-Inhibitory', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.5...
***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Excitatory-Inhibitory', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1...
***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Excitatory-Inhibitory', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.3...
***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Excitatory-Inhibitory', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.4...
***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Excitatory-Inhibitory', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.5...
['Microglial', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'OPC', 'Excitatory-Inhibitory', 'Endothelial', 'Neuroepithelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.75...
***Performing Leiden clustering with resolution 0.5...
['Microglial', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'OPC', 'Excitatory-Inhibitory', 'Endothelial', 'Neuroepithelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.5...
***Performing Leiden clustering with resolution 0.5...
['Microglial', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'OPC', 'Excitatory-Inhibitory', 'Endothelial', 'Neuroepithelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1...
***Performing Leiden clustering with resolution 0.5...
['Oligodendrocyte', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Excitatory-Inhibitory', 'Endothelial']
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 0.3...
***Performing Leiden clustering with resolution 0.5...
['Oligodendrocyte', 'Inhibitory', 'Excitatory', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
{'resolution': 0.5, 'min_dist': 0.3, 'n_comps': 50}
================================================================================
RR10_BRN_GC_WT_G1
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical
***Performing Leiden clustering with resolution 0.2...
... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Excitatory', 'Inhibitory', 'Microglial', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Endothelial']
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_GC_WT_G9
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Microglial', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Inhibitory', 'OPC', 'Neuroepithelial']
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_FLT_WT_F3
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Excitatory', 'Oligodendrocyte', 'Inhibitory', 'OPC', 'Astrocyte', 'Neuroepithelial', 'Microglial']
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_GC_WT_G3
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Inhibitory', 'OPC', 'Astrocyte', 'Neuroepithelial', 'Excitatory', 'Microglial', 'Oligodendrocyte']
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_FLT_WT_F7
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Excitatory', 'Inhibitory', 'Oligodendrocyte', 'Pericyte', 'Microglial', 'OPC', 'Astrocyte', 'Endothelial']
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_FLT_WT_F9
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Oligodendrocyte', 'Astrocyte', 'Excitatory', 'OPC', 'Inhibitory', 'Pericyte', 'Microglial']
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_FLT_WT_F5
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Oligodendrocyte', 'Excitatory', 'Inhibitory', 'Astrocyte', 'OPC', 'Endothelial', 'Microglial', 'Pericyte']
{'resolution': 0.2, 'min_dist': 1.5, 'n_comps': 50}
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
***Calculating PCA with 50 components...
***Building neighborhood...
***Embedding UMAP with minimum distance 1.5...
***Performing Leiden clustering with resolution 0.2...
... storing 'Group' as categorical ... storing 'sample' as categorical ... storing 'Characteristics[Organism]' as categorical ... storing 'Term Source REF' as categorical ... storing 'Term Accession Number' as categorical ... storing 'Characteristics[Strain]' as categorical ... storing 'Term Source REF.1' as categorical ... storing 'Term Accession Number.1' as categorical ... storing 'Characteristics[Animal Source]' as categorical ... storing 'Characteristics[Genotype]' as categorical ... storing 'Term Source REF.2' as categorical ... storing 'Term Accession Number.2' as categorical ... storing 'Characteristics[Sex]' as categorical ... storing 'Term Source REF.3' as categorical ... storing 'Term Accession Number.3' as categorical ... storing 'Characteristics[Material Type]' as categorical ... storing 'Term Source REF.4' as categorical ... storing 'Term Accession Number.4' as categorical ... storing 'Factor Value[Spaceflight]' as categorical ... storing 'Term Source REF.5' as categorical ... storing 'Term Accession Number.5' as categorical ... storing 'Characteristics[Age at Launch]' as categorical ... storing 'Unit' as categorical ... storing 'Term Source REF.6' as categorical ... storing 'Term Accession Number.6' as categorical ... storing 'Characteristics[Age at Euthanasia]' as categorical ... storing 'Unit.1' as categorical ... storing 'Term Source REF.7' as categorical ... storing 'Term Accession Number.7' as categorical ... storing 'Protocol REF' as categorical ... storing 'Parameter Value[habitat]' as categorical ... storing 'Unit.2' as categorical ... storing 'Term Source REF.8' as categorical ... storing 'Term Accession Number.8' as categorical ... storing 'Parameter Value[Enrichment material]' as categorical ... storing 'Parameter Value[light cycle]' as categorical ... storing 'Parameter Value[Diet]' as categorical ... storing 'Parameter Value[Feeding Schedule]' as categorical ... storing 'Parameter Value[Euthanasia Method]' as categorical ... storing 'Parameter Value[Carcass Preservation Method]' as categorical ... storing 'Unit.3' as categorical ... storing 'Term Source REF.9' as categorical ... storing 'Term Accession Number.9' as categorical ... storing 'Protocol REF.1' as categorical ... storing 'Parameter Value[Sample Preservation Method]' as categorical ... storing 'Term Source REF.10' as categorical ... storing 'Term Accession Number.10' as categorical ... storing 'Unit.4' as categorical ... storing 'Term Source REF.11' as categorical ... storing 'Term Accession Number.11' as categorical ... storing 'Comment[RFID]' as categorical ... storing 'Comment[Euthanasia Date]' as categorical ... storing 'Comment[Euthanasia Time (hh:mm:ss)]' as categorical ... storing 'Comment[BSP Dissection Date]' as categorical ... storing 'Comment[Source Description]' as categorical ... storing 'Comment[Partial Body Weight on BSP Dissection Date]' as categorical ... storing 'kws_pp_sample' as categorical ... storing 'feature_types' as categorical ... storing 'genome' as categorical ... storing 'interval' as categorical
['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.75... ***Performing Leiden clustering with resolution 0.2... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.5... ***Performing Leiden clustering with resolution 0.2... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1... ***Performing Leiden clustering with resolution 0.2... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.3... ***Performing Leiden clustering with resolution 0.2... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.4... ***Performing Leiden clustering with resolution 0.2... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1.5... ***Performing Leiden clustering with resolution 0.5... ['Excitatory-Inhibitory', 'OPC', 'Excitatory', 'Inhibitory', 'Astrocyte', 'Oligodendrocyte', 'Pericyte', 'Neuroepithelial', 'Microglial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.75... ***Performing Leiden clustering with resolution 0.5... ['Excitatory-Inhibitory', 'OPC', 'Excitatory', 'Inhibitory', 'Astrocyte', 'Oligodendrocyte', 'Pericyte', 'Neuroepithelial', 'Microglial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.5... ***Performing Leiden clustering with resolution 0.5... ['Excitatory-Inhibitory', 'OPC', 'Excitatory', 'Inhibitory', 'Astrocyte', 'Oligodendrocyte', 'Pericyte', 'Neuroepithelial', 'Microglial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1... ***Performing Leiden clustering with resolution 0.5... ['Excitatory-Inhibitory', 'OPC', 'Excitatory', 'Inhibitory', 'Astrocyte', 'Oligodendrocyte', 'Pericyte', 'Neuroepithelial', 'Microglial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.3... ***Performing Leiden clustering with resolution 0.5... ['Excitatory-Inhibitory', 'OPC', 'Excitatory', 'Inhibitory', 'Astrocyte', 'Oligodendrocyte', 'Pericyte', 'Neuroepithelial', 'Microglial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.4... ***Performing Leiden clustering with resolution 0.5... ['Excitatory-Inhibitory', 'OPC', 'Excitatory', 'Inhibitory', 'Astrocyte', 'Oligodendrocyte', 'Pericyte', 'Neuroepithelial', 'Microglial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1.5... ***Performing Leiden clustering with resolution 0.1... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.75... ***Performing Leiden clustering with resolution 0.1... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.5... ***Performing Leiden clustering with resolution 0.1... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1... ***Performing Leiden clustering with resolution 0.1... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.3... ***Performing Leiden clustering with resolution 0.1... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.4... ***Performing Leiden clustering with resolution 0.1... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1.5... ***Performing Leiden clustering with resolution 0.075... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.75... ***Performing Leiden clustering with resolution 0.075... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.5... ***Performing Leiden clustering with resolution 0.075... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1... ***Performing Leiden clustering with resolution 0.075... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.3... ***Performing Leiden clustering with resolution 0.075... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.4... ***Performing Leiden clustering with resolution 0.075... ['Inhibitory', 'Oligodendrocyte', 'Excitatory', 'Astrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1.5... ***Performing Leiden clustering with resolution 0.15... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.75... ***Performing Leiden clustering with resolution 0.15... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.5... ***Performing Leiden clustering with resolution 0.15... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1... ***Performing Leiden clustering with resolution 0.15... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.3... ***Performing Leiden clustering with resolution 0.15... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 0.4... ***Performing Leiden clustering with resolution 0.15... ['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Neuroepithelial'] ***Calculating PCA with 50 components... ***Building neighborhood... ***Embedding UMAP with minimum distance 1.5... ***Performing Leiden clustering with resolution 0.3...
... storing 'kws_cluster_individual' as categorical
['Inhibitory', 'OPC', 'Excitatory', 'Astrocyte', 'Oligodendrocyte', 'Pericyte', 'Neuroepithelial', 'Microglial']
{'resolution': 0.3, 'min_dist': 1.5, 'n_comps': 50}
No Valid Clustering Scheme Yet: []
================================================================================
RR10_BRN_GC_WT_G5
================================================================================
AnnData object with n_obs × n_vars = 3609 × 25298
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.1dist1.5', 'annotation_by_markers_individual_res0.1dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5', 'leiden_individual_res0.2dist0.75', 'rank_genes_groups_leiden_individual_res0.2dist0.75', 'leiden_individual_res0.2dist0.5', 'rank_genes_groups_leiden_individual_res0.2dist0.5', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1', 'leiden_individual_res0.2dist0.3', 'rank_genes_groups_leiden_individual_res0.2dist0.3', 'leiden_individual_res0.2dist0.4', 'rank_genes_groups_leiden_individual_res0.2dist0.4', 'leiden_individual_res0.5dist1.5', 'rank_genes_groups_leiden_individual_res0.5dist1.5', 'leiden_individual_res0.5dist0.75', 'rank_genes_groups_leiden_individual_res0.5dist0.75', 'leiden_individual_res0.5dist0.5', 'rank_genes_groups_leiden_individual_res0.5dist0.5', 'leiden_individual_res0.5dist1', 'rank_genes_groups_leiden_individual_res0.5dist1', 'leiden_individual_res0.5dist0.3', 'rank_genes_groups_leiden_individual_res0.5dist0.3', 'leiden_individual_res0.5dist0.4', 'rank_genes_groups_leiden_individual_res0.5dist0.4', 'leiden_individual_res0.1dist1.5', 'rank_genes_groups_leiden_individual_res0.1dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_FLT_WT_F1
================================================================================
AnnData object with n_obs × n_vars = 9930 × 30180
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.5dist0.3', 'annotation_by_markers_individual_res0.5dist0.3', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5', 'leiden_individual_res0.2dist0.75', 'rank_genes_groups_leiden_individual_res0.2dist0.75', 'leiden_individual_res0.2dist0.5', 'rank_genes_groups_leiden_individual_res0.2dist0.5', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1', 'leiden_individual_res0.2dist0.3', 'rank_genes_groups_leiden_individual_res0.2dist0.3', 'leiden_individual_res0.2dist0.4', 'rank_genes_groups_leiden_individual_res0.2dist0.4', 'leiden_individual_res0.5dist1.5', 'rank_genes_groups_leiden_individual_res0.5dist1.5', 'leiden_individual_res0.5dist0.75', 'rank_genes_groups_leiden_individual_res0.5dist0.75', 'leiden_individual_res0.5dist0.5', 'rank_genes_groups_leiden_individual_res0.5dist0.5', 'leiden_individual_res0.5dist1', 'rank_genes_groups_leiden_individual_res0.5dist1', 'leiden_individual_res0.5dist0.3', 'rank_genes_groups_leiden_individual_res0.5dist0.3'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_GC_WT_G1
================================================================================
AnnData object with n_obs × n_vars = 6826 × 28485
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_GC_WT_G9
================================================================================
AnnData object with n_obs × n_vars = 6823 × 26600
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_FLT_WT_F3
================================================================================
AnnData object with n_obs × n_vars = 9550 × 29881
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_GC_WT_G3
================================================================================
AnnData object with n_obs × n_vars = 12739 × 30071
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_FLT_WT_F7
================================================================================
AnnData object with n_obs × n_vars = 3994 × 25369
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_FLT_WT_F9
================================================================================
AnnData object with n_obs × n_vars = 2924 × 24858
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_FLT_WT_F5
================================================================================
AnnData object with n_obs × n_vars = 2833 × 24519
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
... storing 'kws_cluster_individual' as categorical
================================================================================
RR10_BRN_GC_WT_G7
================================================================================
AnnData object with n_obs × n_vars = 3098 × 25568
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.3dist1.5', 'annotation_by_markers_individual_res0.3dist1.5', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5', 'leiden_individual_res0.2dist0.75', 'rank_genes_groups_leiden_individual_res0.2dist0.75', 'leiden_individual_res0.2dist0.5', 'rank_genes_groups_leiden_individual_res0.2dist0.5', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1', 'leiden_individual_res0.2dist0.3', 'rank_genes_groups_leiden_individual_res0.2dist0.3', 'leiden_individual_res0.2dist0.4', 'rank_genes_groups_leiden_individual_res0.2dist0.4', 'leiden_individual_res0.5dist1.5', 'rank_genes_groups_leiden_individual_res0.5dist1.5', 'leiden_individual_res0.5dist0.75', 'rank_genes_groups_leiden_individual_res0.5dist0.75', 'leiden_individual_res0.5dist0.5', 'rank_genes_groups_leiden_individual_res0.5dist0.5', 'leiden_individual_res0.5dist1', 'rank_genes_groups_leiden_individual_res0.5dist1', 'leiden_individual_res0.5dist0.3', 'rank_genes_groups_leiden_individual_res0.5dist0.3', 'leiden_individual_res0.5dist0.4', 'rank_genes_groups_leiden_individual_res0.5dist0.4', 'leiden_individual_res0.1dist1.5', 'rank_genes_groups_leiden_individual_res0.1dist1.5', 'leiden_individual_res0.1dist0.75', 'rank_genes_groups_leiden_individual_res0.1dist0.75', 'leiden_individual_res0.1dist0.5', 'rank_genes_groups_leiden_individual_res0.1dist0.5', 'leiden_individual_res0.1dist1', 'rank_genes_groups_leiden_individual_res0.1dist1', 'leiden_individual_res0.1dist0.3', 'rank_genes_groups_leiden_individual_res0.1dist0.3', 'leiden_individual_res0.1dist0.4', 'rank_genes_groups_leiden_individual_res0.1dist0.4', 'leiden_individual_res0.075dist1.5', 'rank_genes_groups_leiden_individual_res0.075dist1.5', 'leiden_individual_res0.075dist0.75', 'rank_genes_groups_leiden_individual_res0.075dist0.75', 'leiden_individual_res0.075dist0.5', 'rank_genes_groups_leiden_individual_res0.075dist0.5', 'leiden_individual_res0.075dist1', 'rank_genes_groups_leiden_individual_res0.075dist1', 'leiden_individual_res0.075dist0.3', 'rank_genes_groups_leiden_individual_res0.075dist0.3', 'leiden_individual_res0.075dist0.4', 'rank_genes_groups_leiden_individual_res0.075dist0.4', 'leiden_individual_res0.15dist1.5', 'rank_genes_groups_leiden_individual_res0.15dist1.5', 'leiden_individual_res0.15dist0.75', 'rank_genes_groups_leiden_individual_res0.15dist0.75', 'leiden_individual_res0.15dist0.5', 'rank_genes_groups_leiden_individual_res0.15dist0.5', 'leiden_individual_res0.15dist1', 'rank_genes_groups_leiden_individual_res0.15dist1', 'leiden_individual_res0.15dist0.3', 'rank_genes_groups_leiden_individual_res0.15dist0.3', 'leiden_individual_res0.15dist0.4', 'rank_genes_groups_leiden_individual_res0.15dist0.4', 'leiden_individual_res0.3dist1.5', 'rank_genes_groups_leiden_individual_res0.3dist1.5'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'distances', 'connectivities'
AAACAGCCAGAAACGT-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
AAACAGCCATTAAGTC-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
AAACATGCAAATGCCC-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
AAACATGCAAGGTATA-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
AAACATGCAGGTTACC-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
...
TTTGTGGCAACCGCCA-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
TTTGTGGCAATCATGT-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
TTTGTGGCATTATGCG-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCCGGTATG-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTCCTCCAA-1 {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 3609, dtype: category
Categories (1, object): ['{'resolution': 0.1, 'min_dist': 1.5, 'n_comps...]
AAACAGCCAATTATGC-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
AAACAGCCACAATACT-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
AAACAGCCAGAACCGA-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
AAACAGCCAGGATGGC-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
AAACAGCCATCACTTC-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
...
TTTGTTGGTACTTCAC-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
TTTGTTGGTCATCCTG-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
TTTGTTGGTCCGCTGT-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
TTTGTTGGTTGTAACG-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
TTTGTTGGTTGTCATC-1 {'resolution': 0.5, 'min_dist': 0.3, 'n_comps'...
Name: kws_cluster_individual, Length: 9930, dtype: category
Categories (1, object): ['{'resolution': 0.5, 'min_dist': 0.3, 'n_comps...]
AAACAGCCAAGTTATC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAGCATGGA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCATAATCCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCAGGACCTT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCAGTAATAG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
...
TTTGTGTTCTTTGAGA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTAAGCACC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTACGTTTC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTTAATCGG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTTGAATCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 6826, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1.5, 'n_comps...]
AAACAGCCACAGGATG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAGCCAGTT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAGGAACAT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAGTTATCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCATAATCCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
...
TTTGTGTTCATCCTGC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCTAAGGAG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTAGCTGGT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTCGCAATA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTGACCTGG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 6823, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1.5, 'n_comps...]
AAACAGCCAAACCTAT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAAGGTACG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAATGCGCT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAGGAACAT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAGTTAGCC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
...
TTTGTGTTCCTAACGG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCTCTAGCC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTATTGTCC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTGACCTGG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTGTCCAAA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 9550, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1.5, 'n_comps...]
AAACAGCCAAGGGTTG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCACCCACAG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCATGATTGT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCATGTTGCA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCAAACTAAG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
...
TTTGTTGGTTAAGGTT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTTAATGCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTTACAAAC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTTGAATCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTTTATCGC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 12739, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1.5, 'n_comps...]
AAACAGCCAATGAGGT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACAGCCAGGTTATT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCAGTAGGTG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACCAACAAGCTTTG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACCAACACAATGTT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
...
TTTGTGAAGGGCTTAT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGGCATTCCTCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTGATGGCT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTGTTTGCT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTTGGTTCTTTAG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 3994, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1.5, 'n_comps...]
AAACATGCAAGGTGCA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCACCTGGTG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCAGCAACAG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCATACTCCT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACCAACAAGGCCAA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
...
TTTGTGAAGTGGCGGA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGAAGTTTCCGC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGGCATGACTAT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGGCATGTCAAT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCATAATCG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 2924, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1.5, 'n_comps...]
AAACAGCCATGAGTTT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCACCAGCAT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACATGCATCCCTCA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACCAACAACCTGGT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
AAACCAACAATGAGGT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
...
TTTGTCCCAAATATCC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTCTAGTTAGTGC-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGGCACCCACCT-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCATGCTAA-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCGCTAGTG-1 {'resolution': 0.2, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 2833, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1.5, 'n_comps...]
AAACCAACACAGGGAC-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
AAACCGAAGAACCTAC-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
AAACCGCGTACGGTAC-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
AAACCGCGTCTCAGCC-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
AAACCGGCATGCTATG-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
...
TTTGTGGCACCTCAGG-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCACTCAAA-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCATCGTTT-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCCGCTAGA-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
TTTGTGTTCTGCAAGT-1 {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'...
Name: kws_cluster_individual, Length: 3098, dtype: category
Categories (1, object): ['{'resolution': 0.3, 'min_dist': 1.5, 'n_comps...]
Concatenate & Integrate¶
If you are concerned about hard drive space, you may want to delete file_concat and files_individual after running this cell.
%%time
# Integration Options
cct_available = kws_cluster is not None and (
markers_predefined is not None) # individual annotations available?
kws_vi = {"early_stopping": True,
"batch_size": 1024, # raise/lower if more/less than 16 GB VRAM
"max_epochs": 100,
"accelerator": "gpu",
"categorical_covariate_keys": covariates_categorical,
"continuous_covariate_keys": covariates_continuous,
"n_latent": 40, "n_hidden": 400} # scVI/scANVI arguments
if covariates_categorical is not None:
print(metadata[0][covariates_categorical + [
col_group]].value_counts().unstack(col_group).replace(
np.nan, 0).astype(int))
kws_integrate = {
"col_celltype": cci_scanvi if cct_available else None,
"flavor": "scanvi",
# "flavor": "scvi",
# "flavor": "scanorama",
# "flavor": "harmony",
"kws_pp": None, "kws_cluster": None,
"vars_regress_out": vars_regress_out,
"max_value": 10, "zero_center": True, "target_sum": 1e4,
"n_top_genes": n_top_genes,
"join": join_method, "merge": "same",
"col_batch": None, # suppress using batch as covariate
"drop_non_hvgs": True, # just for the integration part
"use_rapids": True,
"fill_value": np.nan if join_method == "outer" else None,
"out_file": file_concat, **kws_vi
}
# If scVI/scANVI Integration & Individual Annotations Available
if kws_integrate["flavor"] in ["scvi", "scanvi"] and cct_available is True:
kws_integrate.update({"unlabeled_category": unlabeled_cat})
# Integrate & Store Integration Parameters in Object
self = scflow.Rna(files_individual, col_sample=col_sample,
col_batch=col_batch, kws_integrate=kws_integrate)
self.rna.obs = self.rna.obs.assign(kws_integrate=str(kws_integrate))
# Write Files for Processed/Integrated Objects?
if overwrite is True or not os.path.exists(file_new):
self.rna.write_h5ad(file_new)
# Display
print(self.rna)
self.rna.obs
>>>Concatenating data... >>>Re-Normalizing & Finding HVGs for Overall Data...
>>>Subsetting to top 2000 HVGs... >>>Integrating with respect to sample (SCANVI)...
GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs
***Using counts layer for scanvi...
***Using None, None as covariates...
***Setting up scVI model: {'n_latent': 40, 'n_hidden': 400}...
***Traning scVI: {'max_epochs': 100, 'accelerator': 'gpu', 'batch_size': 1024, 'early_stopping': True}...
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
Training: 0%| | 0/100 [00:00<?, ?it/s]
`Trainer.fit` stopped: `max_epochs=100` reached.
***Setting up scANVI model: {'n_latent': 40, 'n_hidden': 400}...
***Traning scANVI: {'max_epochs': 100, 'accelerator': 'gpu', 'batch_size': 1024, 'early_stopping': True}...
INFO Training for 100 epochs.
GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
Training: 0%| | 0/100 [00:00<?, ?it/s]
`Trainer.fit` stopped: `max_epochs=100` reached. ... storing 'annotation_scanvi' as categorical
count 21692.000000 mean 4589.334686 std 7441.397028 min 34.000000 25% 369.000000 50% 1510.500000 75% 5308.500000 max 62323.000000 Name: n_cells_by_counts, dtype: float64
... storing 'kws_integrate' as categorical
AnnData object with n_obs × n_vars = 62326 × 21692
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Animal Source]', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Characteristics[Material Type]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Spaceflight]', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Age at Launch]', 'Unit', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[Enrichment material]', 'Parameter Value[light cycle]', 'Parameter Value[Diet]', 'Parameter Value[Feeding Schedule]', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Carcass Preservation Method]', 'Parameter Value[Body Weight at Euthanasia]', 'Unit.3', 'Term Source REF.9', 'Term Accession Number.9', 'Protocol REF.1', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.10', 'Term Accession Number.10', 'Parameter Value[Sample Storage Temperature]', 'Unit.4', 'Term Source REF.11', 'Term Accession Number.11', 'Comment[RFID]', 'Comment[ALSDA Subject ID]', 'Comment[Euthanasia Date]', 'Comment[Euthanasia Time (hh:mm:ss)]', 'Comment[BSP Dissection Date]', 'Comment[Source Description]', 'Comment[Partial Body Weight on BSP Dissection Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual', 'annotation_scanvi', 'kws_integrate'
var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
uns: 'log1p', 'hvg', 'sample_colors'
obsm: 'X_pca', 'X_umap', 'X_scANVI', 'X_pca_old'
layers: 'counts', 'log1p', 'scaled'
CPU times: user 3min 37s, sys: 2min 1s, total: 5min 39s
Wall time: 4min 56s
| Group | sample | Characteristics[Organism] | Term Source REF | Term Accession Number | Characteristics[Strain] | Term Source REF.1 | Term Accession Number.1 | Characteristics[Animal Source] | Characteristics[Genotype] | Term Source REF.2 | Term Accession Number.2 | Characteristics[Sex] | Term Source REF.3 | Term Accession Number.3 | Characteristics[Material Type] | Term Source REF.4 | Term Accession Number.4 | Factor Value[Spaceflight] | Term Source REF.5 | Term Accession Number.5 | Characteristics[Age at Launch] | Unit | Term Source REF.6 | Term Accession Number.6 | Characteristics[Age at Euthanasia] | Unit.1 | Term Source REF.7 | Term Accession Number.7 | Protocol REF | Parameter Value[habitat] | Parameter Value[duration] | Unit.2 | Term Source REF.8 | Term Accession Number.8 | Parameter Value[Enrichment material] | Parameter Value[light cycle] | Parameter Value[Diet] | Parameter Value[Feeding Schedule] | Parameter Value[Euthanasia Method] | Parameter Value[Carcass Preservation Method] | Parameter Value[Body Weight at Euthanasia] | Unit.3 | Term Source REF.9 | Term Accession Number.9 | Protocol REF.1 | Parameter Value[Sample Preservation Method] | Term Source REF.10 | Term Accession Number.10 | Parameter Value[Sample Storage Temperature] | Unit.4 | Term Source REF.11 | Term Accession Number.11 | Comment[RFID] | Comment[ALSDA Subject ID] | Comment[Euthanasia Date] | Comment[Euthanasia Time (hh:mm:ss)] | Comment[BSP Dissection Date] | Comment[Source Description] | Comment[Partial Body Weight on BSP Dissection Date] | n_cells_original_sample | kws_pp_sample | n_genes_by_counts | total_counts | log1p_n_genes_by_counts | log1p_total_counts | total_counts_mt | pct_counts_mt | log1p_total_counts_mt | total_counts_ribo | pct_counts_ribo | log1p_total_counts_ribo | total_counts_hb | pct_counts_hb | log1p_total_counts_hb | n_counts | n_genes | doublet_score | predicted_doublet | annotation_by_markers_individual_heterogeneous_collapsed | leiden_individual | annotation_by_markers_individual | kws_cluster_individual | annotation_scanvi | kws_integrate | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AAACAGCCAGAAACGT-1_RR10_BRN_GC_WT_G5 | Ground Control | RR10_BRN_GC_WT_G5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 23.68 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3C500B24 | 800 | 05-Jan-2021 | 06:18:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 4231 | {'min_max_genes': [336, None], 'min_max_cells'... | 2730 | 3351.682373 | 7.912423 | 8.117516 | 21.065426 | 0.628503 | 3.094012 | 26.337223 | 0.785791 | 3.308249 | 0.000000 | 0.000000 | 0.000000 | 7117.0 | 2742 | 0.034528 | False | Excitatory | 2 | Excitatory | {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'... | Excitatory | {'col_celltype': 'annotation_by_markers_indivi... |
| AAACAGCCATTAAGTC-1_RR10_BRN_GC_WT_G5 | Ground Control | RR10_BRN_GC_WT_G5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 23.68 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3C500B24 | 800 | 05-Jan-2021 | 06:18:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 4231 | {'min_max_genes': [336, None], 'min_max_cells'... | 2237 | 3220.274658 | 7.713338 | 8.077533 | 8.841228 | 0.274549 | 2.286581 | 16.175243 | 0.502294 | 2.843469 | 0.000000 | 0.000000 | 0.000000 | 4863.0 | 2246 | 0.059578 | False | Excitatory | 0 | Excitatory | {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'... | Excitatory | {'col_celltype': 'annotation_by_markers_indivi... |
| AAACATGCAAATGCCC-1_RR10_BRN_GC_WT_G5 | Ground Control | RR10_BRN_GC_WT_G5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 23.68 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3C500B24 | 800 | 05-Jan-2021 | 06:18:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 4231 | {'min_max_genes': [336, None], 'min_max_cells'... | 2345 | 3278.046875 | 7.760467 | 8.095308 | 12.397192 | 0.378188 | 2.595045 | 23.126621 | 0.705500 | 3.183316 | 0.000000 | 0.000000 | 0.000000 | 4991.0 | 2353 | 0.086538 | False | Excitatory | 0 | Excitatory | {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'... | Excitatory | {'col_celltype': 'annotation_by_markers_indivi... |
| AAACATGCAAGGTATA-1_RR10_BRN_GC_WT_G5 | Ground Control | RR10_BRN_GC_WT_G5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 23.68 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3C500B24 | 800 | 05-Jan-2021 | 06:18:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 4231 | {'min_max_genes': [336, None], 'min_max_cells'... | 2717 | 3500.315674 | 7.907652 | 8.160894 | 6.348201 | 0.181361 | 1.994455 | 28.441347 | 0.812537 | 3.382400 | 0.964261 | 0.027548 | 0.675116 | 6176.0 | 2731 | 0.119011 | False | Excitatory | 0 | Excitatory | {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'... | Excitatory | {'col_celltype': 'annotation_by_markers_indivi... |
| AAACATGCAGGTTACC-1_RR10_BRN_GC_WT_G5 | Ground Control | RR10_BRN_GC_WT_G5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 23.68 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3C500B24 | 800 | 05-Jan-2021 | 06:18:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 4231 | {'min_max_genes': [336, None], 'min_max_cells'... | 1376 | 2542.810547 | 7.227662 | 7.841418 | 10.975826 | 0.431642 | 2.482890 | 17.603149 | 0.692271 | 2.923331 | 1.580019 | 0.062137 | 0.947797 | 2599.0 | 1381 | 0.065880 | False | Inhibitory | 5 | Inhibitory | {'resolution': 0.1, 'min_dist': 1.5, 'n_comps'... | Inhibitory | {'col_celltype': 'annotation_by_markers_indivi... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| TTTGTGGCACCTCAGG-1_RR10_BRN_GC_WT_G7 | Ground Control | RR10_BRN_GC_WT_G7 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 21.28 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E37071254 | 800 | 05-Jan-2021 | 08:26:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 3926 | {'min_max_genes': [331, None], 'min_max_cells'... | 2022 | 3000.386230 | 7.612337 | 8.006829 | 7.447003 | 0.248201 | 2.133812 | 21.830278 | 0.727582 | 3.128088 | 0.000000 | 0.000000 | 0.000000 | 4570.0 | 2032 | 0.006627 | False | Excitatory | 3 | Excitatory | {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'... | Excitatory | {'col_celltype': 'annotation_by_markers_indivi... |
| TTTGTGTTCACTCAAA-1_RR10_BRN_GC_WT_G7 | Ground Control | RR10_BRN_GC_WT_G7 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 21.28 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E37071254 | 800 | 05-Jan-2021 | 08:26:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 3926 | {'min_max_genes': [331, None], 'min_max_cells'... | 515 | 1493.017090 | 6.246107 | 7.309224 | 12.003338 | 0.803965 | 2.565206 | 21.173267 | 1.418153 | 3.098887 | 0.000000 | 0.000000 | 0.000000 | 697.0 | 523 | 0.063116 | False | Pericyte | 5 | Pericyte | {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'... | Pericyte | {'col_celltype': 'annotation_by_markers_indivi... |
| TTTGTGTTCATCGTTT-1_RR10_BRN_GC_WT_G7 | Ground Control | RR10_BRN_GC_WT_G7 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 21.28 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E37071254 | 800 | 05-Jan-2021 | 08:26:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 3926 | {'min_max_genes': [331, None], 'min_max_cells'... | 2202 | 3061.453125 | 7.697575 | 8.026972 | 4.101108 | 0.133960 | 1.629458 | 21.009678 | 0.686265 | 3.091482 | 0.000000 | 0.000000 | 0.000000 | 5134.0 | 2214 | 0.017561 | False | Excitatory | 3 | Excitatory | {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'... | Excitatory | {'col_celltype': 'annotation_by_markers_indivi... |
| TTTGTGTTCCGCTAGA-1_RR10_BRN_GC_WT_G7 | Ground Control | RR10_BRN_GC_WT_G7 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 21.28 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E37071254 | 800 | 05-Jan-2021 | 08:26:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 3926 | {'min_max_genes': [331, None], 'min_max_cells'... | 2257 | 3195.966797 | 7.722235 | 8.069958 | 23.734081 | 0.742626 | 3.208182 | 39.391556 | 1.232540 | 3.698621 | 0.000000 | 0.000000 | 0.000000 | 4870.0 | 2275 | 0.028606 | False | Excitatory | 11 | Excitatory | {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'... | Excitatory | {'col_celltype': 'annotation_by_markers_indivi... |
| TTTGTGTTCTGCAAGT-1_RR10_BRN_GC_WT_G7 | Ground Control | RR10_BRN_GC_WT_G7 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON... | B6129SF2/J | OSD | https://osdr.nasa.gov/ | Jackson Laboratory | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Ground Control | OSD | https://osdr.nasa.gov/ | 14 - 15 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | 18 -19 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | 28 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | Cocoons | 12 h light/dark cycle, lights on at 7:00 GMT | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | Bilateral thoracotomy with sedation, Ketamine/... | Cryochiller | 21.28 | gram | UO | http://purl.obolibrary.org/obo/UO_0000021 | sample collection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E37071254 | 800 | 05-Jan-2021 | 08:26:00 EST | 14-May-2021 | Frozen carcass dissected on Earth post-flight | Not Available | 3926 | {'min_max_genes': [331, None], 'min_max_cells'... | 1470 | 2666.808594 | 7.293698 | 7.889013 | 12.968199 | 0.486282 | 2.636783 | 24.679865 | 0.925446 | 3.245707 | 0.000000 | 0.000000 | 0.000000 | 2650.0 | 1484 | 0.008061 | False | Astrocyte | 8 | Astrocyte | {'resolution': 0.3, 'min_dist': 1.5, 'n_comps'... | Astrocyte | {'col_celltype': 'annotation_by_markers_indivi... |
62326 rows × 85 columns
Benchmark Integration¶
# if kws_integrate["flavor"] == "scanvi":
# try:
# results_integration_benchmark = scflow.pp.benchmark_integration(
# self.rna, col_sample, col_celltype="annotation_scanvi")
# except Exception as err:
# print(err)
Clustering¶
Perform PCA, UMAP embedding, and Leiden clustering on the integrated object
Marker gene-related code looks at top markers by log2fold-change and adjusted p-value cutoffs and sorts by adjusted p-values. Plots for predefined marker expression by cluster (if available) and cluster DEGs are created. Use kind = ["heat", "dot"] to get dot plots too.
%%time
# Clustering Options
resolution, min_dist = 0.07, 2
# resolution, min_dist = 0.015, 1.5
# resolution, min_dist = 0.022, 1.5
# resolution, min_dist = 0.025, 1
# resolution, min_dist = 0.027, 1.3
# resolution, min_dist = 0.012, 1.5
# resolution, min_dist = 0.027, 1.3
# resolution, min_dist = 0.035, 1.3
# resolution, min_dist = 0.02, 0.6
# resolution, min_dist = 0.018, 0.7
# resolution, min_dist = 0.012, 0.5
# resolution, min_dist = 0.0115, 1.5
# resolution, min_dist = 0.011, 0.5
# n_neighbors = 100
# n_neighbors = 20
n_neighbors = 80
cct = "leiden" # key added/column name
# Set Default Cell Type Column
col_celltype = cct
self._info["col_celltype"] = col_celltype
# Clustering
self.cluster(col_celltype=cct, resolution=resolution, min_dist=min_dist,
kws_pca=False, layer="scaled",
kws_neighbors=dict(n_neighbors=n_neighbors)) # cluster
self.rna.obs = self.rna.obs.assign(**{
f"{cct}_resolution": resolution}).assign(
**{f"{cct}_n_neighbors": n_neighbors}).assign(
**{f"{cct}_min_dist": min_dist}) # store parameters
_ = self.plot(kind="umap", wspace=0.5, palette="tab20",
color=[col_batch, "leiden", col_sample]) # plot UMAP
print(self.rna.obs["leiden"].value_counts().to_frame("n_cells")) # N/cluster
print(self.rna.obs.groupby(col_sample).apply(lambda x: x[
cct].value_counts(), include_groups=False).unstack(1)) # by group
# DEGs (One Cluster versus All)
self.find_markers(col_celltype=cct) # DEGs by cluster
markers_df = self.get_markers_df(
n_genes=15, col_celltype=cct,
p_threshold=1e-10, log2fc_threshold=1.5, log2fc_threshold_abs=False)
markers_dict = dict(markers_df.groupby(cct).apply(
lambda x: list(x.reset_index().names))) # dictionary version of df
_ = self.plot(genes=markers_dict, figsize=(15, 15),
layer="scaled", standard_scale="obs", kind="heat")
if markers_predefined is not None:
mks_c = dict(zip(markers_predefined, [markers_predefined[x].intersection(
self.rna.var_names) for x in markers_predefined]))
mks_c["Inhibitory"] = mks_c["Inhibitory"].difference(mks_c["Excitatory"])
mks_c["Excitatory"] = mks_c["Excitatory"].difference(mks_c["Inhibitory"])
_ = self.plot(genes=mks_c, figsize=(15, 15), vmax=0.7,
layer="scaled", standard_scale="obs", kind="heat")
markers_df
***Building neighborhood with 80 neighbors...
***Embedding UMAP with minimum distance 2...
***Performing Leiden clustering with resolution 0.07...
n_cells
leiden
0 28057
3 10182
4 6475
1 5940
5 4602
6 3056
2 2846
7 1168
leiden 0 1 2 3 4 5 6 7
sample
RR10_BRN_GC_WT_G5 1312 329 259 1073 17 259 240 120
RR10_BRN_FLT_WT_F1 3841 1333 343 1132 1917 855 387 122
RR10_BRN_GC_WT_G1 3722 456 277 1337 0 464 457 113
RR10_BRN_GC_WT_G9 2846 441 620 1717 32 449 520 198
RR10_BRN_FLT_WT_F3 5571 716 724 1278 38 596 422 205
RR10_BRN_GC_WT_G3 5949 1111 271 1016 3149 779 329 135
RR10_BRN_FLT_WT_F7 1817 586 199 657 53 392 215 75
RR10_BRN_FLT_WT_F9 1082 254 92 877 134 284 91 110
RR10_BRN_FLT_WT_F5 835 427 28 599 424 265 207 48
RR10_BRN_GC_WT_G7 1082 287 33 496 711 259 188 42
WARNING: Gene labels are not shown when more than 50 genes are visualized. To show gene labels set `show_gene_labels=True`
WARNING: Gene labels are not shown when more than 50 genes are visualized. To show gene labels set `show_gene_labels=True`
CPU times: user 21.8 s, sys: 1.82 s, total: 23.6 s Wall time: 9.18 s
| scores | logfoldchanges | pvals | pvals_adj | ||
|---|---|---|---|---|---|
| leiden | names | ||||
| 0 | Dpp6 | 115.732620 | 1.922254 | 0.000000e+00 | 0.000000e+00 |
| Nxph1 | 110.448204 | 4.304471 | 0.000000e+00 | 0.000000e+00 | |
| Inpp4b | 105.794746 | 2.575222 | 0.000000e+00 | 0.000000e+00 | |
| Tenm1 | 89.112740 | 1.756814 | 0.000000e+00 | 0.000000e+00 | |
| Adarb2 | 88.722931 | 2.732858 | 0.000000e+00 | 0.000000e+00 | |
| Zfp521 | 43.008194 | 1.591796 | 0.000000e+00 | 0.000000e+00 | |
| Zic1 | 42.607590 | 3.144535 | 0.000000e+00 | 0.000000e+00 | |
| St8sia4 | 41.124001 | 2.063038 | 0.000000e+00 | 0.000000e+00 | |
| Ankfn1 | 39.430374 | 1.889275 | 0.000000e+00 | 0.000000e+00 | |
| Tmem255a | 39.406651 | 2.316693 | 0.000000e+00 | 0.000000e+00 | |
| Lef1 | 39.224106 | 3.134523 | 0.000000e+00 | 0.000000e+00 | |
| Gm45341 | 38.945236 | 2.151298 | 0.000000e+00 | 0.000000e+00 | |
| Insyn2b | 38.894066 | 2.948663 | 0.000000e+00 | 0.000000e+00 | |
| Dock11 | 38.471752 | 1.578298 | 0.000000e+00 | 0.000000e+00 | |
| Col25a1 | 59.337337 | 1.831207 | 0.000000e+00 | 0.000000e+00 | |
| 3 | Ptprd | 204.621841 | 2.562793 | 0.000000e+00 | 0.000000e+00 |
| Kcnq5 | 186.737274 | 2.981804 | 0.000000e+00 | 0.000000e+00 | |
| Nrg1 | 179.353333 | 2.920468 | 0.000000e+00 | 0.000000e+00 | |
| Nrg3 | 175.936340 | 2.308790 | 0.000000e+00 | 0.000000e+00 | |
| Csmd1 | 174.603271 | 2.145468 | 0.000000e+00 | 0.000000e+00 | |
| Celf2 | 167.896011 | 2.210736 | 0.000000e+00 | 0.000000e+00 | |
| Lingo2 | 167.705826 | 2.885069 | 0.000000e+00 | 0.000000e+00 | |
| Nav3 | 165.544708 | 2.181725 | 0.000000e+00 | 0.000000e+00 | |
| Kcnh7 | 164.178696 | 2.971062 | 0.000000e+00 | 0.000000e+00 | |
| Mef2c | 164.118423 | 2.983392 | 0.000000e+00 | 0.000000e+00 | |
| Kalrn | 162.735413 | 2.160930 | 0.000000e+00 | 0.000000e+00 | |
| Dpp10 | 158.968414 | 3.387660 | 0.000000e+00 | 0.000000e+00 | |
| Kctd16 | 118.807434 | 2.200214 | 0.000000e+00 | 0.000000e+00 | |
| Meg3 | 115.624359 | 1.524340 | 0.000000e+00 | 0.000000e+00 | |
| Mlip | 94.051727 | 2.988306 | 0.000000e+00 | 0.000000e+00 | |
| 7 | Erbb4 | 158.453613 | 5.175955 | 0.000000e+00 | 0.000000e+00 |
| Grip1 | 95.315460 | 4.153377 | 0.000000e+00 | 0.000000e+00 | |
| Adarb2 | 94.122925 | 6.116269 | 0.000000e+00 | 0.000000e+00 | |
| Tcf4 | 88.968124 | 2.534769 | 0.000000e+00 | 0.000000e+00 | |
| Snhg11 | 79.849396 | 1.794310 | 0.000000e+00 | 0.000000e+00 | |
| Meg3 | 78.512436 | 1.601473 | 0.000000e+00 | 0.000000e+00 | |
| Galntl6 | 68.108284 | 3.983751 | 0.000000e+00 | 0.000000e+00 | |
| Dlx6os1 | 68.018387 | 5.579965 | 0.000000e+00 | 0.000000e+00 | |
| Zfp536 | 52.880795 | 3.409306 | 5.609284e-317 | 4.345593e-314 | |
| Cntnap2 | 51.291580 | 2.383930 | 4.533505e-313 | 3.391061e-310 | |
| Nrg3 | 43.308628 | 1.546490 | 3.549351e-261 | 1.974167e-258 | |
| Sox2ot | 44.444321 | 3.061857 | 8.076119e-257 | 4.272858e-254 | |
| Sntg1 | 41.728512 | 1.914941 | 1.670851e-238 | 6.970018e-236 | |
| Slc2a13 | 41.869816 | 2.449387 | 1.477733e-237 | 5.828181e-235 | |
| Zmat4 | 40.749634 | 2.873551 | 1.834914e-228 | 6.525074e-226 | |
| 5 | Gpc5 | 191.311218 | 6.749896 | 0.000000e+00 | 0.000000e+00 |
| Slc1a2 | 158.136795 | 5.445472 | 0.000000e+00 | 0.000000e+00 | |
| Kcnn2 | 52.715080 | 2.271234 | 0.000000e+00 | 0.000000e+00 | |
| Nwd1 | 52.641029 | 5.657287 | 0.000000e+00 | 0.000000e+00 | |
| Phka1 | 52.534924 | 3.769710 | 0.000000e+00 | 0.000000e+00 | |
| Macf1 | 51.146343 | 1.702287 | 0.000000e+00 | 0.000000e+00 | |
| F3 | 51.108582 | 5.415951 | 0.000000e+00 | 0.000000e+00 | |
| Rgs20 | 50.708038 | 3.179070 | 0.000000e+00 | 0.000000e+00 | |
| Slc6a11 | 50.149548 | 4.892387 | 0.000000e+00 | 0.000000e+00 | |
| Pla2g7 | 49.924992 | 4.934491 | 0.000000e+00 | 0.000000e+00 | |
| Mt1 | 49.288948 | 3.417319 | 0.000000e+00 | 0.000000e+00 | |
| Bcan | 49.166378 | 4.112677 | 0.000000e+00 | 0.000000e+00 | |
| Gm20713 | 48.207859 | 4.497130 | 0.000000e+00 | 0.000000e+00 | |
| Rmst | 47.923149 | 2.810101 | 0.000000e+00 | 0.000000e+00 | |
| Appl2 | 47.763943 | 3.331866 | 0.000000e+00 | 0.000000e+00 | |
| 2 | Celf2 | 140.025757 | 1.843905 | 0.000000e+00 | 0.000000e+00 |
| Nrg3 | 128.191055 | 1.955656 | 0.000000e+00 | 0.000000e+00 | |
| Grin2a | 118.028183 | 2.536846 | 0.000000e+00 | 0.000000e+00 | |
| Csmd1 | 111.173851 | 1.603665 | 0.000000e+00 | 0.000000e+00 | |
| Grm5 | 110.561127 | 1.936699 | 0.000000e+00 | 0.000000e+00 | |
| Meg3 | 110.402870 | 1.667266 | 0.000000e+00 | 0.000000e+00 | |
| Epha6 | 83.489983 | 2.999736 | 0.000000e+00 | 0.000000e+00 | |
| Pde1a | 81.820412 | 2.680102 | 0.000000e+00 | 0.000000e+00 | |
| Tafa1 | 81.360863 | 3.866324 | 0.000000e+00 | 0.000000e+00 | |
| Tenm2 | 79.103287 | 1.967414 | 0.000000e+00 | 0.000000e+00 | |
| Nav3 | 78.408066 | 1.516752 | 0.000000e+00 | 0.000000e+00 | |
| Gm20754 | 60.253918 | 2.426447 | 0.000000e+00 | 0.000000e+00 | |
| Cttnbp2 | 60.196331 | 1.744821 | 0.000000e+00 | 0.000000e+00 | |
| Shisa6 | 60.036594 | 3.209508 | 0.000000e+00 | 0.000000e+00 | |
| Gria3 | 59.887257 | 1.714430 | 0.000000e+00 | 0.000000e+00 | |
| 1 | Plp1 | 412.365082 | 7.318904 | 0.000000e+00 | 0.000000e+00 |
| Pde4b | 232.140137 | 4.342822 | 0.000000e+00 | 0.000000e+00 | |
| Mbp | 206.797119 | 5.418245 | 0.000000e+00 | 0.000000e+00 | |
| Nkain2 | 202.465622 | 3.081276 | 0.000000e+00 | 0.000000e+00 | |
| Plcl1 | 186.147675 | 4.822429 | 0.000000e+00 | 0.000000e+00 | |
| Pcdh9 | 184.831909 | 2.371376 | 0.000000e+00 | 0.000000e+00 | |
| St18 | 183.953705 | 6.560849 | 0.000000e+00 | 0.000000e+00 | |
| Cdk19 | 66.729935 | 3.306992 | 0.000000e+00 | 0.000000e+00 | |
| Cdc37l1 | 66.473412 | 2.961321 | 0.000000e+00 | 0.000000e+00 | |
| Erbb4 | 64.296539 | 2.400096 | 0.000000e+00 | 0.000000e+00 | |
| Cldn11 | 64.223267 | 5.166214 | 0.000000e+00 | 0.000000e+00 | |
| Tbc1d5 | 63.344940 | 2.439339 | 0.000000e+00 | 0.000000e+00 | |
| Aspa | 62.900658 | 5.280020 | 0.000000e+00 | 0.000000e+00 | |
| Apod | 62.140999 | 4.926096 | 0.000000e+00 | 0.000000e+00 | |
| Gm16168 | 61.738800 | 4.230247 | 0.000000e+00 | 0.000000e+00 | |
| 6 | Hs3st4 | 208.010193 | 4.877477 | 0.000000e+00 | 0.000000e+00 |
| Nrg3 | 165.524002 | 2.407715 | 0.000000e+00 | 0.000000e+00 | |
| Ptprd | 160.860458 | 2.074726 | 0.000000e+00 | 0.000000e+00 | |
| Etl4 | 149.539642 | 3.490088 | 0.000000e+00 | 0.000000e+00 | |
| Cdh18 | 125.096443 | 3.583939 | 0.000000e+00 | 0.000000e+00 | |
| Csmd1 | 120.169815 | 1.799085 | 0.000000e+00 | 0.000000e+00 | |
| Nav3 | 115.913101 | 2.040621 | 0.000000e+00 | 0.000000e+00 | |
| Nalf1 | 112.029533 | 1.917980 | 0.000000e+00 | 0.000000e+00 | |
| Frmpd4 | 104.698502 | 2.777440 | 0.000000e+00 | 0.000000e+00 | |
| Syt1 | 101.681335 | 1.767520 | 0.000000e+00 | 0.000000e+00 | |
| Prickle1 | 69.170570 | 2.165214 | 0.000000e+00 | 0.000000e+00 | |
| Garnl3 | 68.942566 | 3.106019 | 0.000000e+00 | 0.000000e+00 | |
| Slc8a1 | 67.668358 | 1.795062 | 0.000000e+00 | 0.000000e+00 | |
| Fut9 | 66.745811 | 2.127027 | 0.000000e+00 | 0.000000e+00 | |
| Ryr2 | 66.688759 | 1.769109 | 0.000000e+00 | 0.000000e+00 | |
| 4 | Phactr1 | 170.293198 | 3.297709 | 0.000000e+00 | 0.000000e+00 |
| Cacna2d3 | 161.046173 | 3.269646 | 0.000000e+00 | 0.000000e+00 | |
| Rarb | 156.014038 | 5.616486 | 0.000000e+00 | 0.000000e+00 | |
| Ryr3 | 134.184540 | 3.132653 | 0.000000e+00 | 0.000000e+00 | |
| Dgkb | 123.956688 | 2.777103 | 0.000000e+00 | 0.000000e+00 | |
| Rgs9 | 122.611969 | 5.302050 | 0.000000e+00 | 0.000000e+00 | |
| Grm5 | 119.174614 | 2.292377 | 0.000000e+00 | 0.000000e+00 | |
| Celf2 | 117.857468 | 2.183239 | 0.000000e+00 | 0.000000e+00 | |
| Meis2 | 116.666595 | 3.646053 | 0.000000e+00 | 0.000000e+00 | |
| Kcnq5 | 116.074730 | 2.755988 | 0.000000e+00 | 0.000000e+00 | |
| Pde10a | 114.707039 | 2.759833 | 0.000000e+00 | 0.000000e+00 | |
| Sgcz | 111.795059 | 2.839822 | 0.000000e+00 | 0.000000e+00 | |
| Adcy5 | 109.838974 | 4.025107 | 0.000000e+00 | 0.000000e+00 | |
| Osbpl8 | 73.052383 | 2.243474 | 0.000000e+00 | 0.000000e+00 | |
| Elmod1 | 72.750404 | 2.141031 | 0.000000e+00 | 0.000000e+00 |
Sub-Clustering¶
Sub-cluster the biggest <subcluster_biggest> Leiden clusters?
For instance if subcluster_biggest = 3, take top three most abundant (highest cell count) clusters and sub-cluster them.
resolution_sub = None if subcluster_biggest is False else 0.005
if subcluster_biggest is not False:
# Sub-Clustering
col_celltype = "leiden_subcluster"
biggest_clusters = self.rna.obs["leiden"].value_counts().index.values[
:min(len(self.rna.obs["leiden"].unique()), subcluster_biggest)]
self.rna.obs.loc[:, col_celltype] = self.rna.obs.leiden.copy()
for x in biggest_clusters:
sc.tl.leiden(self.rna, restrict_to=(col_celltype, [x]),
key_added=col_celltype, resolution=resolution_sub,
seed=0, n_iterations=-1, use_weights=True)
self.find_markers(col_celltype=col_celltype) # DEGs by cluster
self.plot(kind="umap", color=col_celltype)
print(self.rna.obs[[col_sample, col_celltype]].groupby(
col_sample).value_counts().unstack(1))
self.rna.obs.loc[:, f"resolution_{col_celltype}"] = resolution_sub
self._info["col_celltype"] = col_celltype
# Marker Plots
if markers_predefined is not None:
mks_c = dict(zip(markers_predefined, [markers_predefined[
x].intersection(self.rna.var_names) for x in markers_predefined]))
_ = self.plot(genes=mks_c, figsize=(15, 15), vmax=0.7,
layer="scaled", standard_scale="obs", kind="heat")
markers_df = self.get_markers_df(
n_genes=15, col_celltype=cct,
p_threshold=1e-10, log2fc_threshold=1.5, log2fc_threshold_abs=False)
markers_dict = dict(markers_df.groupby(cct).apply(
lambda x: list(x.reset_index().names))) # dictionary version of df
_ = self.plot(genes=markers_dict, figsize=(15, 15),
layer="scaled", standard_scale="obs", kind="heat")
print(markers_df)
Annotate¶
Annotate cell types with various methods
Annotate by Marker Gene Overlap¶
Example of the Expected Marker Definition Format
markers_predefined = {
"CD4 T cells": {"IL7R"},
"CD14+ Monocytes": {"CD14", "LYZ"},
"B cells": {"MS4A1"},
"CD8 T cells": {"CD8A"},
"NK cells": {"GNLY", "NKG7"},
"FCGR3A+ Monocytes": {"FCGR3A", "MS4A7"},
"Dendritic Cells": {"FCER1A", "CST3"},
"Megakaryocytes": {"PPBP"},
}
# Plot
_ = self.plot(genes=mks_c, figsize=(15, 15),
layer="scaled", standard_scale="var", kind="matrix")
# Annotate
marker_matches = self.annotate(
mks_collapsed,
# celltypes_superhierarchical=celltypes_superhierarchical,
col_celltype=col_celltype, col_celltype_new="annotation_by_overlap",
top_n_markers=50, # can only have this one or `adj_pval_threshold`
# adj_pval_threshold=1e-10,
# method="overlap_count",
method="overlap_coef",
# method="jaccard",
overwrite=True)
# Plot Predefined Marker Expression
# cct = col_celltype
# # cct = "annotation_by_markers_individual"
# mks_col = dict(zip(self.rna.obs[cct].cat.categories, [list(
# mks_collapsed[x].intersection(self.rna.var_names)
# ) for x in self.rna.obs[cct].cat.categories]))
# _ = self.plot(genes=mks_col, figsize=(15, 15), col_celltype=cct,
# layer="scaled", standard_scale="obs", kind="heat")
# Rename Any Labels As Pre-Specified
if rename_marker_based_annotation is not None:
self.rna.obs.loc[:, "annotation_by_overlap"] = self.rna.obs[
"annotation_by_overlap"].replace(rename_marker_based_annotation)
# Print & Plot Results
self.plot(kind="umap", color="annotation_by_overlap", wspace=0.4)
print(round(self.rna.obs["annotation_by_overlap"].value_counts(
normalize=True) * 100, 2))
round(self.rna.obs[[col_celltype, "annotation_by_overlap"]
].value_counts(normalize=True)* 100, 2).sort_values()
annotation_by_overlap OPC 49.92 Neuron 33.17 Oligodendrocyte 9.53 Astrocyte 7.38 Name: proportion, dtype: float64
leiden annotation_by_overlap 7 Neuron 1.87 2 Neuron 4.57 6 OPC 4.90 5 Astrocyte 7.38 1 Oligodendrocyte 9.53 4 Neuron 10.39 3 Neuron 16.34 0 OPC 45.02 Name: proportion, dtype: float64
Annotate with ToppGene¶
# Options
min_genes = 2 # minimum markers that have to overlap between Leiden & atlas
remove_strings = ["----L1-6", # "---[|]M.*",
"facs-", "-nan-",
# "-i_Gaba_3-.*",
"Brain_organoid-organoid_Kanton_Nature-Organoid-..-",
# "Non-neuronal-Macroglial-((^|)(Oligo|Astro))+-",
# "-Glut_E.*IL7R",
"cells hierarchy compared to all cells using T-S.*",
".*-organoid_Tanaka_cellReport-.+-",
"...BrainAtlas -.*", "-eN2.*", "...Sample groups.*",
"...Sample Type, Dataset.*",
"-Neuronal",
" // Primary Cells by Cluster",
".World...Primary Cells by Cluster",
"Brain_organoid-organoid_Velasco_nature-6_",
"Fetal_brain-fetalBrain_Zhong_nature-....-",
"Somatosensory_Cortex_....-Neuronal-",
"Non-neuronal-Non-dividing-",
"...Sample groups..6 Anatomical region groups., with 5.*",
"Brain_organoid-organoid_Paulsen_bioRxiv-",
"-Glut_E_(THEMIS)", "[(]THEMIS[)]", # "[|].*",
"- method, tissue, subtissue, age, lineage.*"]
drop_name_patterns = ["striatum", "globus", "Entopeduncular",
"Substantia_nigra-", "Thalamus-"]
toppgene_rename_by_pattern = dict(
Inhibitory=["Inh(_|ib)", "GABA"], Excitatory=["Excit", "Glut"],
# Inhibitory=["Inh(_|ib)"], Excitatory=["Excit"],
# # Gabaergic=["GABA"], Glutamatergic=["Glut"],
Astrocyte=["Astrocyte","Astroglia", "Macroglial-Astro"],
Microglial=["Microglia", "Micro"],
Endothelial=["Endothelial"],
Oligodendrocyte=[r"^(?=.*oligo)(?!.*poly)(?!.*opc).*"],
OPC=["Polydendrocyte", "OPC"])
drop_regions = [
"Mid-temporal_gyrus_(MTG)", "primary_auditory_cortex_(A1C)",
"Somatosensory_Cortex_(S1)", "Anterior_Cingulate_gyrus_(CgG)",
"Primary_Motor_Cortex_(M1)",
"Mid-temporal_gyrus_(MTG)|Mid-temporal_gyrus_(MTG)",
"primary_auditory_cortex_(A1C)|primary_auditory_cortex_(A1C)",
"Somatosensory_Cortex_(S1)|Somatosensory_Cortex_(S1)",
"Anterior_Cingulate_gyrus_(CgG)|Anterior_Cingulate_gyrus_(CgG)",
"Primary_Motor_Cortex_(M1)|Primary_Motor_Cortex_(M1)",
r"Neuronal|World / ",
"Primary_Visual_cortex_(V1C)|Primary_Visual_cortex_(V1C)",
"mon",
"BMP_responsible_cell|6m", "bearing_cell|6m", "bearing_cell|GW16", "11",
"Non-neuronal-Non-dividing",
"Frontal_cortex|Frontal_cortex",
"Primary_Visual_cortex_(V1C)", "Substantia_nigra",
"Thalamus", "Hippocampus", "Frontal_cortex"
] # remove if name is just a region or top-level hierarchical/undesired type
drop_regions = drop_regions + [f"{i}-Non-neuronal" for i in drop_regions]
# Query ToppGene
results_toppgene = scflow.pp.annotate_by_toppgene(
markers_dict, remove_strings=remove_strings,
species=species, min_genes=min_genes, source_patterns=source_patterns)
# Remove or Alter Certain Name Patterns
drop_names = results_toppgene.Name.apply(lambda x: not any((
i.lower() in x.lower() for i in drop_name_patterns)))
results_toppgene = results_toppgene[drop_names]
rn_tg = results_toppgene.Name.apply(lambda x: {x: " | ".join([
j for j in toppgene_rename_by_pattern if any((re.search(i.lower(
), x.lower()) is not None for i in toppgene_rename_by_pattern[
j]))])}).apply(lambda x: np.nan if x[list(x.keys())[
0]] == "" else x).dropna().reset_index(drop=True).apply(
lambda x: pd.Series(x)).stack().reset_index(
0, drop=True) # renaming guide
results_toppgene = results_toppgene.replace({"Name": dict(rn_tg)})
results_toppgene = results_toppgene[~results_toppgene.Name.isin(drop_regions)]
# Map Labels (Plurality Vote If Sufficient or Top)
top_cs = dict(results_toppgene.groupby(
"Gene Set").apply(lambda x: x.Name.iloc[:10].value_counts().index.values[
0] if x.Name.iloc[:10].value_counts(
normalize=True).iloc[0] >= 0.25 else x.Name[0]))
print("\n".join([f"{k}: {top_cs[k]}" for k in top_cs]), "\n\n")
if "annotation_toppgene" in self.rna.obs:
self.rna.obs = self.rna.obs.drop("annotation_toppgene", axis=1)
self.rna.obs = self.rna.obs.join(self.rna.obs[col_celltype].replace(
top_cs).to_frame("annotation_toppgene")).loc[self.rna.obs.index]
# Display Results
if "annotation_by_overlap" in self.rna.obs:
print(round(self.rna.obs[["annotation_toppgene", "annotation_by_overlap"]
].value_counts(normalize=True).sort_index(
) * 100, 2), "\n\n")
print(round(self.rna.obs["annotation_toppgene"].value_counts(
normalize=True) * 100, 2))
results_toppgene.reset_index("ID", drop=True).drop([
"QValueBonferroni", "QValueFDRBY", "QValueFDRBH",
"TotalGenes", "Genes"], axis=1)
Server returned HTTP status code: 400
Content: {'error': 'No valid lookup found for symbol Gm20754'}
0: Inhibitory
1: Oligodendrocyte
2: Excitatory
3: Excitatory
4: Excitatory
5: Astrocyte
6: Excitatory
7: Inhibitory
annotation_toppgene annotation_by_overlap
Inhibitory OPC 45.02
Neuron 1.87
Oligodendrocyte Oligodendrocyte 9.53
Excitatory OPC 4.90
Neuron 31.29
Astrocyte Astrocyte 7.38
Name: proportion, dtype: float64
annotation_toppgene
Inhibitory 46.89
Excitatory 36.20
Oligodendrocyte 9.53
Astrocyte 7.38
Name: proportion, dtype: float64
| Name | PValue | GenesInTerm | GenesInQuery | GenesInTermInQuery | Source | Name_Original | percent_atlas_genes_in_query | Symbols | ||
|---|---|---|---|---|---|---|---|---|---|---|
| Gene Set | Category | |||||||||
| 0 | ToppCell | Inhibitory | 0.0 | 180 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.777778 | [TENM1, ANKFN1, ADARB2, INPP4B, COL25A1] |
| ToppCell | Inhibitory | 0.0 | 184 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.717391 | [TENM1, DOCK11, DPP6, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 187 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.673797 | [TENM1, DOCK11, DPP6, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.631579 | [ADARB2, TMEM255A, NXPH1, COL25A1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.57732 | [DOCK11, ADARB2, NXPH1, COL25A1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.57732 | [TENM1, DOCK11, ADARB2, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 196 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.55102 | [TENM1, DOCK11, ADARB2, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory|Neuronal / cells hierarchy... | 2.5 | [TENM1, DOCK11, ADARB2, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iB|Neuronal / cells hierar... | 2.5 | [TENM1, DOCK11, NXPH1, COL25A1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 176 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.272727 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 176 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.272727 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 177 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.259887 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 177 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.259887 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 178 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.247191 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 179 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.234637 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 181 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.209945 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 182 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.197802 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 182 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.197802 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 183 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.185792 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 183 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.185792 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 183 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.185792 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 183 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.185792 | [TMEM255A, NXPH1, COL25A1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 183 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.185792 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 184 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.173913 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 184 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.173913 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 184 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.173913 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 185 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.162162 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 185 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.162162 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 186 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.150538 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 186 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.150538 | [ADARB2, INPP4B, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 186 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.150538 | [ADARB2, TMEM255A, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 186 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.150538 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 187 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.139037 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 187 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.139037 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 188 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.12766 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 188 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.12766 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 188 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.12766 | [ADARB2, INPP4B, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 188 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.12766 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 188 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.12766 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 189 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.116402 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 189 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.116402 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 189 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.116402 | [DOCK11, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 189 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.116402 | [ADARB2, INPP4B, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.105263 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.105263 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.105263 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.105263 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.105263 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.105263 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.105263 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.105263 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.105263 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.105263 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.105263 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 191 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.094241 | [DOCK11, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 191 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.094241 | [ADARB2, INPP4B, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 191 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.094241 | [ADARB2, INPP4B, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 192 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.083333 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 192 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.083333 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 192 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.083333 | [DOCK11, ADARB2, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 192 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.083333 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 192 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.083333 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 192 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.083333 | [TENM1, DOCK11, ADARB2, NXPH1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.072539 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.072539 | [DOCK11, ADARB2, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.072539 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.072539 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.072539 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.072539 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 193 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.072539 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.061856 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.061856 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.061856 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.061856 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.061856 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.061856 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.061856 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iB-iB_4(SST)-ADGRG6-|Neuro... | 2.0 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iB-iB_4(SST)-B3GAT2|Neuron... | 2.0 | [DOCK11, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_2(LAMP5)-CA1-|Neuron... | 2.0 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iB-iB_4(SST)|Neuronal / ce... | 2.0 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iB-iB_4(SST)-CALB1--L1-3|N... | 2.0 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_2(LAMP5)-CA1|Neurona... | 2.0 | [ADARB2, TMEM255A, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iB-iB_2(PVALB)-SULF1--L4-6... | 2.0 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iB-iB_4(SST)-STK32A|Neuron... | 2.0 | [TENM1, DOCK11, NXPH1, COL25A1] | |
| ToppCell | Inhibitory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Inhibitory-iB-iB_3(PVALB-SST)|Neurona... | 2.0 | [TENM1, DOCK11, NXPH1, ST8SIA4] | |
| 1 | ToppCell | Oligodendrocyte | 0.0 | 188 | 14 | 7 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Non-neuronal-Mac... | 3.723404 | [ST18, PLP1, CLDN11, PDE4B, MBP, ASPA, APOD] |
| ToppCell | Oligodendrocyte | 0.0 | 188 | 14 | 7 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 3.723404 | [ST18, PLP1, CLDN11, PDE4B, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 200 | 14 | 7 | Human Adult Brain MTG | Macroglial-Oligodendrocytes|Macroglial / cells... | 3.5 | [NKAIN2, ST18, PLP1, PCDH9, CLDN11, PLCL1, MBP] | |
| ToppCell | Oligodendrocyte | 0.0 | 200 | 14 | 7 | Human Adult Brain MTG | Macroglial-Oligodendrocytes-OPALIN---|Macrogli... | 3.5 | [NKAIN2, ST18, PLP1, PCDH9, CLDN11, PLCL1, MBP] | |
| ToppCell | Oligodendrocyte | 0.0 | 200 | 14 | 7 | Human Adult Brain MTG | Macroglial-Oligodendrocytes-OPALIN-|Macroglial... | 3.5 | [NKAIN2, ST18, PLP1, PCDH9, CLDN11, PLCL1, MBP] | |
| ToppCell | Oligodendrocyte | 0.0 | 200 | 14 | 7 | Human Adult Brain MTG | Macroglial-Oligodendrocytes-OPALIN----L1-6|Mac... | 3.5 | [NKAIN2, ST18, PLP1, PCDH9, CLDN11, PLCL1, MBP] | |
| ToppCell | Oligodendrocyte | 0.0 | 200 | 14 | 7 | Human Adult Brain MTG | Macroglial-Oligodendrocytes-OPALIN--|Macroglia... | 3.5 | [NKAIN2, ST18, PLP1, PCDH9, CLDN11, PLCL1, MBP] | |
| ToppCell | Oligodendrocyte | 0.0 | 200 | 14 | 7 | Human Adult Brain MTG | Macroglial-Oligodendrocytes-OPALIN|Macroglial ... | 3.5 | [NKAIN2, ST18, PLP1, PCDH9, CLDN11, PLCL1, MBP] | |
| ToppCell | Oligodendrocyte | 0.0 | 183 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 3.278689 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 183 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 3.278689 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 184 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 3.26087 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 184 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 3.26087 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 184 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 3.26087 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 185 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... | 3.243243 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 185 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... | 3.243243 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 186 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 3.225806 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 186 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 3.225806 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 0.0 | 187 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 3.208556 | [ST18, PLP1, CLDN11, ERBB4, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 187 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Non-neuronal-Mac... | 3.208556 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 187 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Non-neuronal-Mac... | 3.208556 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 188 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 3.191489 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 188 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... | 3.191489 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 189 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 3.174603 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | OPC | 0.0 | 189 | 14 | 6 | Comparison of human cortex and organoids | Non-neuronal-Non-dividing-OPC|World / Primary ... | 3.174603 | [PLP1, PCDH9, CLDN11, PDE4B, MBP, APOD] | |
| ToppCell | OPC | 0.0 | 189 | 14 | 6 | Comparison of human cortex and organoids | Non-neuronal-Non-dividing-OPC-OPC|World / Prim... | 3.174603 | [PLP1, PCDH9, CLDN11, PDE4B, MBP, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 189 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 3.174603 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | OPC | 0.0 | 189 | 14 | 6 | Comparison of human cortex and organoids | Non-neuronal-Non-dividing-OPC-OPC-30|World / P... | 3.174603 | [PLP1, PCDH9, CLDN11, PDE4B, MBP, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 190 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 3.157895 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 0.0 | 190 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 3.157895 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 191 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 3.141361 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Primary_Motor_Cortex_(M1)-Non-neuronal|Primary... | 0.0 | 191 | 14 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal|Primary... | 3.141361 | [ST18, PLP1, CLDN11, MBP, ASPA, APOD] | |
| ToppCell | Oligodendrocyte | 0.0 | 122 | 14 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Macroglia-OLIGODENDROCYTE-O2|Fr... | 4.098361 | [ST18, PLP1, CLDN11, MBP, ASPA] | |
| ToppCell | Oligodendrocyte | 0.0 | 122 | 14 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Macroglia-OLIGODENDROCYTE-O2-Tn... | 4.098361 | [ST18, PLP1, CLDN11, MBP, ASPA] | |
| ToppCell | Oligodendrocyte | 0.0 | 122 | 14 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Macroglia-OLIGODENDROCYTE|Front... | 4.098361 | [ST18, PLP1, CLDN11, MBP, ASPA] | |
| ToppCell | Oligodendrocyte | 0.0 | 124 | 14 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Posterior_cortex-Macroglia-OLIGODENDROCYTE|Pos... | 4.032258 | [ST18, PLP1, CLDN11, MBP, ASPA] | |
| ToppCell | Oligodendrocyte | 0.0 | 132 | 14 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-OLIGODENDROCYTE-O2-Trf|H... | 3.787879 | [PLP1, CDC37L1, CLDN11, MBP, ASPA] | |
| ToppCell | Oligodendrocyte | 0.0 | 132 | 14 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-OLIGODENDROCYTE-O2|Hippo... | 3.787879 | [PLP1, CDC37L1, CLDN11, MBP, ASPA] | |
| ToppCell | Oligodendrocyte | 0.0 | 137 | 14 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-OLIGODENDROCYTE|Hippocam... | 3.649635 | [PLP1, CDC37L1, CLDN11, MBP, ASPA] | |
| ToppCell | Oligodendrocyte | 0.0 | 181 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 2.762431 | [ST18, PLP1, CLDN11, MBP, ASPA] | |
| ToppCell | Primary_Visual_cortex_(V1C)-Non-neuronal|Prima... | 0.0 | 185 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal|Prima... | 2.702703 | [ST18, PLP1, CLDN11, ASPA, APOD] | |
| ToppCell | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 0.0 | 188 | 14 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 2.659574 | [ST18, PLP1, CLDN11, ASPA, APOD] | |
| 2 | ToppCell | Excitatory | 0.0 | 195 | 14 | 7 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 3.589744 | [NAV3, CELF2, TENM2, GRIN2A, MEG3, CSMD1, NRG3] |
| ToppCell | Excitatory | 0.0 | 197 | 14 | 5 | Comparison of human cortex and organoids | Neuron-Postmitotic-Excitatory_Neuron_-Deep_Lay... | 2.538071 | [GRM5, TENM2, PDE1A, SHISA6, MEG3] | |
| ToppCell | Excitatory | 0.0 | 187 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 2.139037 | [CELF2, TENM2, PDE1A, TAFA1] | |
| ToppCell | Excitatory | 0.0 | 189 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 2.116402 | [CELF2, TENM2, PDE1A, TAFA1] | |
| ToppCell | Excitatory | 0.0 | 190 | 14 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 2.105263 | [TENM2, GRIN2A, CTTNBP2, TAFA1] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Comparison of human cortex and organoids | Neuron-Postmitotic-Inhibitory_Neuron-MGE2|Worl... | 2.061856 | [GRM5, SHISA6, GRIN2A, MEG3] | |
| ToppCell | Inhibitory | 0.0 | 194 | 14 | 4 | Comparison of human cortex and organoids | Neuron-Postmitotic-Inhibitory_Neuron-MGE2-10|W... | 2.061856 | [GRM5, SHISA6, GRIN2A, MEG3] | |
| ToppCell | OPC | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Macroglial-Polydendrocytes-PDGFRA----L1-6|Macr... | 2.0 | [GRM5, GRIA3, MEG3, CSMD1] | |
| ToppCell | OPC | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Macroglial-Polydendrocytes-PDGFRA|Macroglial /... | 2.0 | [GRM5, GRIA3, MEG3, CSMD1] | |
| ToppCell | OPC | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Macroglial-Polydendrocytes-PDGFRA---|Macroglia... | 2.0 | [GRM5, GRIA3, MEG3, CSMD1] | |
| ToppCell | OPC | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Macroglial-Polydendrocytes-PDGFRA-|Macroglial ... | 2.0 | [GRM5, GRIA3, MEG3, CSMD1] | |
| ToppCell | OPC | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Macroglial-Polydendrocytes-PDGFRA--|Macroglial... | 2.0 | [GRM5, GRIA3, MEG3, CSMD1] | |
| ToppCell | Excitatory | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Neuronal-Excitatory|Neuronal / cells hierarchy... | 2.0 | [NAV3, CELF2, PDE1A, TAFA1] | |
| ToppCell | OPC | 0.0 | 200 | 14 | 4 | Human Adult Brain MTG | Macroglial-Polydendrocytes|Macroglial / cells ... | 2.0 | [GRM5, GRIA3, MEG3, CSMD1] | |
| ToppCell | Excitatory | 0.000011 | 143 | 14 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Posterior_cortex-Neuronal-Excitatory-eN1(Slc17... | 2.097902 | [EPHA6, GRIA3, TAFA1] | |
| ToppCell | Inhibitory | 0.000018 | 167 | 14 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Cerebellum-Neuronal-Inhibitory-iN1(Gad1Gad2_Th... | 1.796407 | [GRIA3, SHISA6, MEG3] | |
| ToppCell | Inhibitory | 0.000018 | 167 | 14 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Cerebellum-Neuronal-Inhibitory|Cerebellum / Br... | 1.796407 | [GRIA3, SHISA6, MEG3] | |
| ToppCell | Excitatory | 0.000018 | 168 | 14 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Neuronal-Excitatory-eN1(Slc17a7)|H... | 1.785714 | [PDE1A, GRIN2A, TAFA1] | |
| ToppCell | Excitatory | 0.000023 | 181 | 14 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 1.657459 | [EPHA6, TENM2, TAFA1] | |
| ToppCell | Excitatory | 0.000024 | 186 | 14 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 1.612903 | [CELF2, PDE1A, TAFA1] | |
| 3 | ToppCell | Excitatory | 0.0 | 195 | 15 | 11 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 5.641026 | [NAV3, CELF2, KALRN, NRG1, MEF2C, MEG3, MLIP, ... |
| ToppCell | Excitatory | 0.0 | 192 | 15 | 8 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Glutamaterg... | 4.166667 | [CELF2, KALRN, NRG1, LINGO2, MLIP, DPP10, PTPR... | |
| ToppCell | Excitatory | 0.0 | 189 | 15 | 7 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 3.703704 | [CELF2, KALRN, LINGO2, MLIP, DPP10, PTPRD, KCNQ5] | |
| ToppCell | Excitatory | 0.0 | 190 | 15 | 7 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 3.684211 | [CELF2, KALRN, NRG1, LINGO2, MLIP, PTPRD, KCNQ5] | |
| ToppCell | Excitatory | 0.0 | 190 | 15 | 7 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 3.684211 | [CELF2, KALRN, NRG1, LINGO2, MLIP, PTPRD, KCNQ5] | |
| ToppCell | Excitatory | 0.0 | 200 | 15 | 7 | Human Adult Brain MTG | Neuronal-Excitatory|Neuronal / cells hierarchy... | 3.5 | [NAV3, CELF2, KALRN, MLIP, DPP10, PTPRD, KCNQ5] | |
| ToppCell | Posterior_cortex|World | 0.0 | 159 | 15 | 6 | Mouse Adult Brain Overview (690k cells, 9 regi... | Posterior_cortex|World / BrainAtlas - Mouse Mc... | 3.773585 | [KALRN, MEF2C, KCNH7, KCTD16, MLIP, KCNQ5] | |
| ToppCell | Excitatory | 0.0 | 187 | 15 | 6 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 3.208556 | [CELF2, KALRN, LINGO2, MLIP, PTPRD, KCNQ5] | |
| ToppCell | Excitatory | 0.0 | 188 | 15 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 3.191489 | [CELF2, KALRN, LINGO2, MLIP, PTPRD, KCNQ5] | |
| ToppCell | Excitatory | 0.0 | 189 | 15 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 3.174603 | [CELF2, KALRN, LINGO2, MLIP, PTPRD, KCNQ5] | |
| ToppCell | Excitatory | 0.0 | 186 | 15 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 2.688172 | [CELF2, KALRN, NRG1, MLIP, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 192 | 15 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 2.604167 | [NRG1, MEF2C, LINGO2, KCNH7, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 174 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 2.298851 | [NRG1, LINGO2, MLIP, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 190 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 2.105263 | [MEF2C, LINGO2, KCTD16, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 195 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 2.051282 | [KALRN, MEF2C, LINGO2, PTPRD] | |
| ToppCell | Neuron | 0.0 | 199 | 15 | 4 | Comparison of human cortex and organoids | Neuron|World / Primary Cells by Cluster | 2.01005 | [CELF2, MEF2C, MEG3, PTPRD] | |
| ToppCell | Neuron-Postmitotic | 0.0 | 199 | 15 | 4 | Comparison of human cortex and organoids | Neuron-Postmitotic|World / Primary Cells by Cl... | 2.01005 | [CELF2, MEF2C, MEG3, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 200 | 15 | 4 | Comparison of human cortex and organoids | Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... | 2.0 | [CELF2, MEF2C, MEG3, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 200 | 15 | 4 | Comparison of human cortex and organoids | Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... | 2.0 | [CELF2, MEF2C, MEG3, PTPRD] | |
| ToppCell | Cortical_neuron|GW09 | 0.0 | 200 | 15 | 4 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-organoid_Tanaka_cellReport-GW09-Ne... | 2.0 | [CELF2, MEF2C, KCNH7, PTPRD] | |
| ToppCell | Frontal_cortex|World | 0.000021 | 164 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex|World / BrainAtlas - Mouse McCa... | 1.829268 | [MEF2C, KCNH7, KCNQ5] | |
| ToppCell | Excitatory | 0.000023 | 170 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 1.764706 | [NRG1, LINGO2, MLIP] | |
| ToppCell | Excitatory | 0.000025 | 173 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 1.734104 | [NRG1, KCNH7, DPP10] | |
| ToppCell | Excitatory | 0.000025 | 174 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 1.724138 | [NRG1, KCNH7, DPP10] | |
| ToppCell | Excitatory | 0.000025 | 175 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Excitatory-eN1(Slc17a7... | 1.714286 | [KALRN, MEF2C, KCNH7] | |
| ToppCell | Excitatory | 0.000025 | 175 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Excitatory-eN1(Slc17a7... | 1.714286 | [KALRN, MEF2C, KCNH7] | |
| ToppCell | Excitatory | 0.000026 | 176 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 1.704545 | [LINGO2, KCTD16, MLIP] | |
| 4 | ToppCell | Excitatory | 0.0 | 170 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 2.352941 | [SGCZ, RGS9, MEIS2, RYR3] |
| ToppCell | Excitatory | 0.0 | 189 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 2.116402 | [CELF2, CACNA2D3, KCNQ5, PHACTR1] | |
| ToppCell | Excitatory | 0.0 | 189 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 2.116402 | [CELF2, CACNA2D3, KCNQ5, PHACTR1] | |
| ToppCell | Neuronal-ventral_progenitors_and_neurons_1|Org... | 0.0 | 190 | 15 | 4 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Kanton_Nature-Organoid... | 2.105263 | [GRM5, ELMOD1, MEIS2, CACNA2D3] | |
| ToppCell | Excitatory | 0.0 | 190 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 2.105263 | [CELF2, CACNA2D3, KCNQ5, PHACTR1] | |
| ToppCell | Excitatory | 0.0 | 190 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 2.105263 | [CELF2, CACNA2D3, KCNQ5, PHACTR1] | |
| ToppCell | Excitatory | 0.0 | 192 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Glutamaterg... | 2.083333 | [CELF2, CACNA2D3, KCNQ5, PHACTR1] | |
| ToppCell | Excitatory | 0.0 | 195 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 2.051282 | [CELF2, CACNA2D3, KCNQ5, PHACTR1] | |
| ToppCell | Excitatory | 0.00001 | 128 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Cerebellum-Neuronal-Excitatory-eN2(Slc17a7_Slc... | 2.34375 | [SGCZ, RGS9, KCNQ5] | |
| ToppCell | Excitatory | 0.00001 | 128 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Cerebellum-Neuronal-Excitatory-eN2(Slc17a7_Slc... | 2.34375 | [SGCZ, RGS9, KCNQ5] | |
| ToppCell | Excitatory | 0.000023 | 168 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 1.785714 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000025 | 173 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 1.734104 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000025 | 174 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 1.724138 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000025 | 174 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 1.724138 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000025 | 175 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 1.714286 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000025 | 175 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Excitatory-eN1(Slc17a7... | 1.714286 | [GRM5, DGKB, MEIS2] | |
| ToppCell | Excitatory | 0.000025 | 175 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Excitatory-eN1(Slc17a7... | 1.714286 | [GRM5, DGKB, MEIS2] | |
| ToppCell | Excitatory | 0.000026 | 176 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 1.704545 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000026 | 176 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 1.704545 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000026 | 177 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 1.694915 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000027 | 178 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 1.685393 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000027 | 179 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 1.675978 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000027 | 179 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 1.675978 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000028 | 180 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 1.666667 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000028 | 180 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Glutamaterg... | 1.666667 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000028 | 181 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Glutamaterg... | 1.657459 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000029 | 183 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 1.639344 | [RGS9, MEIS2, RYR3] | |
| ToppCell | Excitatory | 0.000031 | 187 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 1.604278 | [CELF2, KCNQ5, PHACTR1] | |
| ToppCell | Excitatory | 0.000031 | 188 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 1.595745 | [CELF2, KCNQ5, PHACTR1] | |
| 5 | ToppCell | Astrocyte | 0.0 | 151 | 12 | 6 | Mouse Adult Brain Overview (690k cells, 9 regi... | Cerebellum-Macroglia-ASTROCYTE-Gja1|Cerebellum... | 3.97351 | [PLA2G7, NWD1, F3, RGS20, SLC1A2, SLC6A11] |
| ToppCell | Astrocyte | 0.0 | 151 | 12 | 6 | Mouse Adult Brain Overview (690k cells, 9 regi... | Cerebellum-Macroglia-ASTROCYTE|Cerebellum / Br... | 3.97351 | [PLA2G7, NWD1, F3, RGS20, SLC1A2, SLC6A11] | |
| ToppCell | Astrocyte | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 2.673797 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 2.673797 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 2.673797 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 2.673797 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 2.673797 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 2.673797 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 190 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... | 2.631579 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 190 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Non-neuronal-Mac... | 2.631579 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 190 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... | 2.631579 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 190 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... | 2.631579 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 192 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Non-neuronal-Mac... | 2.604167 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 192 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 2.604167 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 192 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Non-neuronal-Mac... | 2.604167 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 193 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 2.590674 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 193 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 2.590674 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Macroglial-Astrocytes|Macroglial / cells hiera... | 2.5 | [NWD1, GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 121 | 12 | 4 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-ASTROCYTE-Gja1-Astrocyte... | 3.305785 | [PLA2G7, NWD1, GPC5, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 121 | 12 | 4 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-ASTROCYTE-Gja1-Astrocyte... | 3.305785 | [PLA2G7, NWD1, GPC5, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 121 | 12 | 4 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-ASTROCYTE-Gja1-Astrocyte... | 3.305785 | [PLA2G7, NWD1, GPC5, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 187 | 12 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Non-neuronal-Mac... | 2.139037 | [NWD1, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 196 | 12 | 4 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW23-Macro... | 2.040816 | [GPC5, PHKA1, F3, RGS20] | |
| ToppCell | Macroglial|GW23 | 0.0 | 196 | 12 | 4 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW23-Macro... | 2.040816 | [GPC5, PHKA1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-SLC14A1|Macroglial / cel... | 2.0 | [GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Macroglial|GW23 | 0.0 | 200 | 12 | 4 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-organoid_Tanaka_cellReport-GW23-Ma... | 2.0 | [BCAN, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-GFAP---|Macroglial / cel... | 2.0 | [NWD1, GPC5, F3, RGS20] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW26-Macro... | 2.0 | [BCAN, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-GFAP-|Macroglial / cells... | 2.0 | [NWD1, GPC5, F3, RGS20] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-GFAP--|Macroglial / cell... | 2.0 | [NWD1, GPC5, F3, RGS20] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-GFAP|Macroglial / cells ... | 2.0 | [NWD1, GPC5, F3, RGS20] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-SLC14A1-|Macroglial / ce... | 2.0 | [GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-SLC14A1---|Macroglial / ... | 2.0 | [GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-GFAP----L1-2|Macroglial ... | 2.0 | [NWD1, GPC5, F3, RGS20] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-SLC14A1----L1-6|Macrogli... | 2.0 | [GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.0 | 200 | 12 | 4 | Human Adult Brain MTG | Macroglial-Astrocytes-SLC14A1--|Macroglial / c... | 2.0 | [GPC5, F3, RGS20, SLC1A2] | |
| ToppCell | Macroglial|GW26 | 0.0 | 200 | 12 | 4 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW26-Macro... | 2.0 | [BCAN, F3, RGS20, SLC1A2] | |
| ToppCell | Astrocyte | 0.000004 | 119 | 12 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-ASTROCYTE|Hippocampus / ... | 2.521008 | [PLA2G7, NWD1, SLC1A2] | |
| ToppCell | Astrocyte | 0.000004 | 119 | 12 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Macroglia-ASTROCYTE-Gja1|Hippocamp... | 2.521008 | [PLA2G7, NWD1, SLC1A2] | |
| ToppCell | Astrocyte | 0.000012 | 172 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... | 1.744186 | [NWD1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000013 | 178 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 1.685393 | [NWD1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000014 | 181 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... | 1.657459 | [NWD1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000015 | 186 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 1.612903 | [NWD1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000015 | 186 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... | 1.612903 | [NWD1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000015 | 187 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... | 1.604278 | [NWD1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000016 | 190 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 1.578947 | [NWD1, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000016 | 190 | 12 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... | 1.578947 | [NWD1, F3, RGS20] | |
| ToppCell | Radial_Glia-oRG-26 | 0.000016 | 192 | 12 | 3 | Comparison of human cortex and organoids | Non-neuronal-Non-dividing-Radial_Glia-oRG-26|W... | 1.5625 | [BCAN, F3, RGS20] | |
| ToppCell | Brain_organoid-organoid_Velasco_nature-3_mon-R... | 0.000018 | 198 | 12 | 3 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Velasco_nature-3_mon-R... | 1.515152 | [BCAN, F3, RGS20] | |
| ToppCell | Brain_organoid-organoid_Velasco_nature-3_mon-R... | 0.000018 | 199 | 12 | 3 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Velasco_nature-3_mon-R... | 1.507538 | [BCAN, F3, RGS20] | |
| ToppCell | 3.5_mon-Radial_glial-oRG|3.5_mon | 0.000018 | 199 | 12 | 3 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Paulsen_bioRxiv-3.5_mo... | 1.507538 | [BCAN, F3, RGS20] | |
| ToppCell | mon-Radial_glial-oRG|6_mon | 0.000018 | 199 | 12 | 3 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Velasco_nature-6_mon-R... | 1.507538 | [BCAN, F3, SLC1A2] | |
| ToppCell | Astrocyte | 0.000018 | 200 | 12 | 3 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Tanaka_cellReport-3m-M... | 1.5 | [BCAN, F3, RGS20] | |
| ToppCell | Astrocyte | 0.000018 | 200 | 12 | 3 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Tanaka_cellReport-6m-M... | 1.5 | [BCAN, F3, SLC1A2] | |
| ToppCell | Macroglial|3m | 0.000018 | 200 | 12 | 3 | Integration of Four Brain Organoid Datasets an... | Brain_organoid-organoid_Tanaka_cellReport-3m-M... | 1.5 | [BCAN, F3, RGS20] | |
| 6 | ToppCell | Excitatory | 0.0 | 195 | 15 | 10 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... | 5.128205 | [NAV3, SYT1, SLC8A1, RYR2, NALF1, PTPRD, CSMD1... |
| ToppCell | Excitatory | 0.0 | 190 | 15 | 6 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Glutama... | 3.157895 | [PRICKLE1, SLC8A1, RYR2, CDH18, PTPRD, HS3ST4] | |
| ToppCell | Excitatory | 0.0 | 200 | 15 | 6 | Human Adult Brain MTG | Neuronal-Excitatory|Neuronal / cells hierarchy... | 3.0 | [NAV3, PRICKLE1, RYR2, CDH18, PTPRD, HS3ST4] | |
| ToppCell | Excitatory | 0.0 | 189 | 15 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 2.645503 | [PRICKLE1, RYR2, CDH18, PTPRD, HS3ST4] | |
| ToppCell | Excitatory | 0.0 | 190 | 15 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... | 2.631579 | [PRICKLE1, SLC8A1, RYR2, CDH18, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 192 | 15 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Glutamaterg... | 2.604167 | [PRICKLE1, SLC8A1, RYR2, CDH18, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 150 | 15 | 4 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Excitatory-eN2(Slc17a7... | 2.666667 | [FUT9, CDH18, GARNL3, HS3ST4] | |
| ToppCell | Excitatory | 0.0 | 153 | 15 | 4 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Excitatory-eN2(Slc17a7... | 2.614379 | [FUT9, CDH18, GARNL3, HS3ST4] | |
| ToppCell | Excitatory | 0.0 | 186 | 15 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 2.150538 | [PRICKLE1, RYR2, CDH18, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 200 | 15 | 4 | Comparison of human cortex and organoids | Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... | 2.0 | [SYT1, SLC8A1, RYR2, PTPRD] | |
| ToppCell | Excitatory | 0.0 | 200 | 15 | 4 | Comparison of human cortex and organoids | Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... | 2.0 | [SYT1, SLC8A1, RYR2, PTPRD] | |
| ToppCell | Excitatory | 0.000021 | 163 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Posterior_cortex-Neuronal-Excitatory-eN2(Slc17... | 1.840491 | [FUT9, CDH18, HS3ST4] | |
| ToppCell | Excitatory | 0.000023 | 170 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... | 1.764706 | [KIAA1217, GARNL3, HS3ST4] | |
| ToppCell | Excitatory | 0.000025 | 174 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 1.724138 | [SLC8A1, KIAA1217, HS3ST4] | |
| ToppCell | Excitatory | 0.000026 | 177 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 1.694915 | [KIAA1217, CDH18, HS3ST4] | |
| ToppCell | Excitatory | 0.000026 | 177 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Glutamaterg... | 1.694915 | [SLC8A1, KIAA1217, HS3ST4] | |
| ToppCell | Excitatory | 0.000027 | 179 | 15 | 3 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... | 1.675978 | [KIAA1217, CDH18, HS3ST4] | |
| ToppCell | Excitatory | 0.00003 | 184 | 15 | 3 | Mouse Adult Brain Overview (690k cells, 9 regi... | Posterior_cortex-Neuronal-Excitatory|Posterior... | 1.630435 | [FUT9, RYR2, NRG3] | |
| 7 | ToppCell | Inhibitory | 0.0 | 169 | 12 | 9 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Inhibitory|Frontal_cor... | 5.325444 | [ZNF536, ERBB4, GRIP1, GALNTL6, SNTG1, SLC2A13... |
| ToppCell | Inhibitory | 0.0 | 159 | 12 | 7 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Inhibitory-iN2(Gad1Gad... | 4.402516 | [ZNF536, ERBB4, GRIP1, ZMAT4, SLC2A13, ADARB2,... | |
| ToppCell | Inhibitory | 0.0 | 167 | 12 | 7 | Mouse Adult Brain Overview (690k cells, 9 regi... | Posterior_cortex-Neuronal-Inhibitory|Posterior... | 4.191617 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2, TCF4, ... | |
| ToppCell | Inhibitory | 0.0 | 194 | 12 | 7 | Comparison of human cortex and organoids | Neuron-Postmitotic-Inhibitory_Neuron-MGE2|Worl... | 3.608247 | [ZNF536, ERBB4, GALNTL6, MEG3, ADARB2, TCF4, C... | |
| ToppCell | Inhibitory | 0.0 | 194 | 12 | 7 | Comparison of human cortex and organoids | Neuron-Postmitotic-Inhibitory_Neuron-MGE2-10|W... | 3.608247 | [ZNF536, ERBB4, GALNTL6, MEG3, ADARB2, TCF4, C... | |
| ToppCell | Inhibitory | 0.0 | 196 | 12 | 7 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 3.571429 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2, TCF4, ... | |
| ToppCell | Inhibitory | 0.0 | 191 | 12 | 6 | Comparison of human cortex and organoids | Neuron-Postmitotic-Inhibitory_Neuron|World / P... | 3.141361 | [ZNF536, ERBB4, GALNTL6, MEG3, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 191 | 12 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 3.141361 | [ZNF536, ERBB4, GRIP1, ZMAT4, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 192 | 12 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 3.125 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 192 | 12 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 3.125 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 192 | 12 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 3.125 | [ZNF536, ERBB4, GRIP1, ADARB2, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 193 | 12 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 3.108808 | [ZNF536, ERBB4, GRIP1, ADARB2, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 194 | 12 | 6 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 3.092784 | [ZNF536, ERBB4, GRIP1, ADARB2, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 194 | 12 | 6 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 3.092784 | [ZNF536, ERBB4, GRIP1, ADARB2, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 6 | Human Adult Brain MTG | Neuronal-Inhibitory|Neuronal / cells hierarchy... | 3.0 | [ZNF536, ERBB4, GRIP1, ADARB2, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 6 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW26-Neuro... | 3.0 | [ZNF536, ERBB4, GRIP1, MEG3, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 6 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_1(SST_PAX6)|Neuronal... | 3.0 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 6 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_3(VIP)-VIP_3|Neurona... | 3.0 | [ZNF536, ERBB4, GALNTL6, ADARB2, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 6 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_3(VIP)|Neuronal / ce... | 3.0 | [ZNF536, ERBB4, GALNTL6, ADARB2, TCF4, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 155 | 12 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Posterior_cortex-Neuronal-Inhibitory-iN2(Gad1G... | 3.225806 | [ZNF536, ERBB4, GRIP1, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 169 | 12 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Neuronal-Inhibitory-iN2(Gad1Gad2)|... | 2.95858 | [ZNF536, ERBB4, GRIP1, SLC2A13, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 181 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.762431 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 182 | 12 | 5 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Neuronal-Inhibitory|Hippocampus / ... | 2.747253 | [ZNF536, ERBB4, GRIP1, SLC2A13, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 184 | 12 | 5 | Comparison of human cortex and organoids | Neuron-Postmitotic-Inhibitory_Neuron-SST-MGE1-... | 2.717391 | [ZNF536, ERBB4, MEG3, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 185 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.702703 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 185 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.702703 | [ZNF536, ERBB4, GALNTL6, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 186 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.688172 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 186 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.688172 | [ZNF536, ERBB4, GRIP1, ZMAT4, GALNTL6] | |
| ToppCell | Inhibitory | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.673797 | [ZNF536, ERBB4, GALNTL6, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 187 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.673797 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 188 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.659574 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 188 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... | 2.659574 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 189 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... | 2.645503 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 191 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.617801 | [ZNF536, ERBB4, GALNTL6, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 191 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Mid-temporal_gyrus_(MTG)-Neuronal-Inh_GABAergi... | 2.617801 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 192 | 12 | 5 | Comparison of human cortex and organoids | Neuron-Postmitotic-Inhibitory_Neuron-SST-MGE1|... | 2.604167 | [ZNF536, ERBB4, MEG3, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 194 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.57732 | [ZNF536, ERBB4, GRIP1, ZMAT4, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 194 | 12 | 5 | Human Adult Multiple Cortical Areas SMART-seq | Anterior_Cingulate_gyrus_(CgG)-Neuronal-Inh_GA... | 2.57732 | [ZNF536, ERBB4, GRIP1, ZMAT4, ADARB2] | |
| ToppCell | Fetal_brain-fetalBrain_Zhong_nature-GW23|fetal... | 0.0 | 199 | 12 | 5 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW23|fetal... | 2.512563 | [ZNF536, ERBB4, MEG3, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_3(VIP)-VIP_3-CBLN1-L... | 2.5 | [ZNF536, ERBB4, GALNTL6, ADARB2, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_1(SST_PAX6)-NMBR-|Ne... | 2.5 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_1(SST_PAX6)-NMBR|Neu... | 2.5 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_3(VIP)-VIP_3-CHRM2|N... | 2.5 | [ZNF536, ERBB4, GALNTL6, ADARB2, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_3(VIP)-VIP_3-CHRM2-L... | 2.5 | [ZNF536, ERBB4, GALNTL6, ADARB2, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW23-Neuro... | 2.5 | [ZNF536, ERBB4, MEG3, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA|Neuronal / cells hierar... | 2.5 | [ZNF536, ERBB4, GRIP1, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_1(SST_PAX6)-NMBR--L1... | 2.5 | [ZNF536, ERBB4, GRIP1, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 200 | 12 | 5 | Human Adult Brain MTG | Neuronal-Inhibitory-iA-iA_3(VIP)-VIP_3-CBLN1|N... | 2.5 | [ZNF536, ERBB4, GALNTL6, ADARB2, CNTNAP2] | |
| ToppCell | Inhibitory | 0.0 | 139 | 12 | 4 | Mouse Adult Brain Overview (690k cells, 9 regi... | Frontal_cortex-Neuronal-Inhibitory-iN2(Gad1Gad... | 2.877698 | [ERBB4, SLC2A13, ADARB2, TCF4] | |
| ToppCell | Inhibitory | 0.0 | 161 | 12 | 4 | Mouse Adult Brain Overview (690k cells, 9 regi... | Hippocampus-Neuronal-Inhibitory-iN1(Gad1Gad2_T... | 2.484472 | [ZNF536, ERBB4, GRIP1, SLC2A13] | |
| ToppCell | Inhibitory | 0.0 | 164 | 12 | 4 | Integration of Four Brain Organoid Datasets an... | Fetal_brain-fetalBrain_Zhong_nature-GW16-Neuro... | 2.439024 | [ZNF536, ERBB4, MEG3, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 169 | 12 | 4 | Human Adult Multiple Cortical Areas SMART-seq | Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... | 2.366864 | [ZNF536, ERBB4, GALNTL6, ADARB2] | |
| ToppCell | Inhibitory | 0.0 | 171 | 12 | 4 | Human Adult Multiple Cortical Areas SMART-seq | primary_auditory_cortex_(A1C)-Neuronal-Inh_GAB... | 2.339181 | [ZNF536, ERBB4, GALNTL6, ADARB2] |
Annotate with CellTypist¶
%%time
# To Aggregate More Specific Cell Types
celltypist_rename = dict(
Gabaergic=["GABA"], Glutamatergic=["Glut"],
Dopaminergic=["Dopa"], Serotonergic=["Sero"],
Inhibitory=["Inh"], Excitatory=["Exc"],
Astrocyte=["Astro"],
Microglia=["Microglia"],
Pericyte=["peri"],
Monocyte=["Monocyte"],
Lymphoid=["Lymphoid"],
Endothelial=["Endothelial", "Endo"],
Oligodendrocyte=[r"^(?=.*oligo)(?!.*poly)(?!.*opc).*"],
OPC=["OPC", "Polydend"])
# Run CellTypist
self.rna.X = self.rna.layers["counts"].copy()
sc.pp.normalize_total(self.rna, target_sum=10000)
sc.pp.log1p(self.rna) # copy=True: do not update adata.X
predictions = self.annotate(
model_celltypist, col_celltype=col_celltype, layer=None,
col_celltype_new="", majority_voting=True, min_prop=0.5, use_GPU=True)
if "majority_voting" in self.rna.obs:
self.rna.obs.loc[:, "majority_voting_short"] = self.rna.obs[
"majority_voting"].apply(lambda x: " ".join(x.split(
" ")[1:]) if all((i in [str(i) for i in np.arange(
0, 10)] for i in x.split(" ")[
0])) else x) # drop pointless #s in front of cell types
# Rename Cell Types
rn_ct = predictions.predicted_labels.groupby("majority_voting").apply(
lambda x: {x.name: " | ".join([
j for j in celltypist_rename if any((re.search(
i.lower(), x.name.lower()) for i in celltypist_rename[
j]))])}).apply(lambda x: {list(x.keys())[0]: list(x.keys(
))[0]} if x[list(x.keys())[0] ] == "" else x).apply(
lambda x: pd.Series(x)).stack().reset_index(
0, drop=True)
if "annotation_majority_voting" in self.rna.obs:
self.rna.obs = self.rna.obs.drop("annotation_majority_voting", axis=1)
self.rna.obs = self.rna.obs.join(self.rna.obs.replace({
"majority_voting": dict(rn_ct)})["majority_voting"].to_frame(
"annotation_majority_voting"))
self.rna.X = self.rna.layers["scaled"].copy()
WARNING: adata.X seems to be already log-transformed.
2025-09-01 16:57:34 | [INFO] 🔬 Input data has 62326 cells and 21692 genes 2025-09-01 16:57:34 | [INFO] 🔗 Matching reference genes in the model 2025-09-01 16:57:35 | [INFO] 🧬 4758 features used for prediction 2025-09-01 16:57:35 | [INFO] ⚖️ Scaling input data 2025-09-01 16:57:47 | [INFO] 🖋️ Predicting labels 2025-09-01 16:57:47 | [INFO] ✅ Prediction done! 2025-09-01 16:57:47 | [INFO] 👀 Detected a neighborhood graph in the input object, will run over-clustering on the basis of it 2025-09-01 16:57:47 | [INFO] ⛓️ Over-clustering input data with resolution set to 20 2025-09-01 16:57:49 | [INFO] 🗳️ Majority voting the predictions 2025-09-01 16:57:50 | [INFO] ✅ Majority voting done!
<timed exec>:30: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
CPU times: user 19.8 s, sys: 31.6 s, total: 51.5 s Wall time: 41.1 s
Annotate with Map My Cells¶
Make sure to run the following bash commonds after activating the conda environment used for this notebook.
Pull
cell_type_mapperfrom GitHub (clone into your home directory):cd && git clone git@github.com:AllenInstitute/cell_type_mapper.gitNavigate to that directory and run
pip install .Navigate to the folder containing this notebook.
Install ABC Atlas (while in same directory as this notebook):
pip install -U git+https://github.com/alleninstitute/abc_atlas_access >& scratch/junk.txtPull lookup files (while in same directory as this notebook):
cd resources
wget https://allen-brain-cell-atlas.s3-us-west-2.amazonaws.com/mapmycells/WMB-10X/20240831/mouse_markers_230821.json
wget https://allen-brain-cell-atlas.s3-us-west-2.amazonaws.com/mapmycells/WMB-10X/20240831/precomputed_stats_ABC_revision_230821.h5
Note: To use GPU + Torch, you may need to alter the file "cell_type_mapper/src/cell_type_mapper/cell_by_gene/cell_by_gene.py" line np.where(np.logical_not(np.isfinite(data)))[0] to read instead
try:
nan_rows = np.where(
np.logical_not(np.isfinite(data.cpu().numpy())))[0]
except Exception:
nan_rows = np.where(np.logical_not(np.isfinite(data)))[0]
You may have to run the following code in this notebook:
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["OMP_NUM_THREADS"] = "1"
and
`_correlation_dot_gpu()` in distance_utils.py change
`correlation = torch.matmul(arr0, arr1)` to
try:
correlation = torch.matmul(arr0, arr1)
except RuntimeError as err:
if "CUBLAS_STATUS_NOT_INITIALIZED" in str(err):
arr0_cpu = arr0.cpu()
arr1_cpu = arr1.cpu()
correlation = torch.matmul(arr0_cpu, arr1_cpu).to(arr0.device)
else:
raise
to manage processes/memory.
# %%time
# # Write File to Use as Input for Map My Cells
# if overwrite is True or not os.path.exists(file_new):
# os.makedirs("data", exist_ok=True)
# self.rna.X = self.rna.layers["counts"]
# self.rna.write_h5ad(file_new)
# else:
# raise ValueError("Must be able to overwrite to run Map My Cells.")
# # Run Map My Cells
# self.rna = scflow.pp.run_mapbraincells(
# file_new, map_my_cells_source=map_my_cells_source,
# dir_scratch="scratch", dir_resources="resources",
# validate_output_file="scratch/tmp.h5ad", # map_to_ensembl=True,
# map_my_cells_region_keys=map_my_cells_region_keys,
# map_my_cells_cell_keys=map_my_cells_cell_keys, verbose_stdout=True,
# n_processors=4, chunk_size=5000, max_gb=5)
# View Results
# _ = self.plot(kind="umap", color=["cellmap_class_name"])
# if "annotation_toppgene" in self.rna.obs:
# print(self.rna.obs[["cellmap_class_name", "annotation_toppgene"]
# ].value_counts().sort_index())
# self.rna.obs[[i for i in self.rna.obs if "cellmap" in i and "ori" not in i]]
Compare Annotations¶
cols = ["annotation_by_overlap", "annotation_scanvi",
"annotation_toppgene", "annotation_majority_voting"]
# cols += [i for i in [
# "cellmap_class_name", "cellmap_subclass_name"] if i in self.rna.obs]
cols = [i for i in cols if i in self.rna.obs]
# Plot UMAPs
self.plot(kind="umap", color=cols, wspace=0.3)
# Compare
# self.rna.obs[cols].value_counts().reset_index().groupby(cols[0]).apply(
# lambda x: x.sort_values("count", ascending=False).reset_index(
# drop=True), include_groups=False).reset_index(
# -1, drop=True).set_index(cols[1:], append=True)
self.rna.obs[cols].groupby(cols[0]).apply(
lambda x: round(100 * x.value_counts(normalize=True), 1).sort_index(
).sort_values(ascending=False), include_groups=False)
annotation_by_overlap annotation_scanvi annotation_toppgene annotation_majority_voting
OPC Inhibitory Inhibitory Heterogeneous 17.5
Excitatory Inhibitory Glutamatergic 16.3
Inhibitory Inhibitory Gabaergic 15.3
Excitatory Excitatory Glutamatergic 8.6
Inhibitory Heterogeneous 6.8
Oligodendrocyte Inhibitory Heterogeneous 5.9
OPC Inhibitory OPC 5.1
Microglial Inhibitory Glutamatergic 4.3
Inhibitory Inhibitory Glutamatergic 3.4
Oligodendrocyte Inhibitory Glutamatergic 2.3
OPC Inhibitory Heterogeneous 1.9
Astrocyte Inhibitory Heterogeneous 1.9
Microglial Inhibitory Microglia 1.6
Oligodendrocyte Inhibitory Oligodendrocyte 1.2
Excitatory Inhibitory Gabaergic 1.1
Excitatory Heterogeneous 0.9
Pericyte Inhibitory Glutamatergic 0.8
OPC Inhibitory Gabaergic 0.7
Microglial Inhibitory Heterogeneous 0.4
OPC Inhibitory Oligodendrocyte 0.4
Oligodendrocyte Inhibitory Gabaergic 0.3
Pericyte Inhibitory 330 VLMC NN 0.3
Oligodendrocyte Inhibitory 330 VLMC NN 0.3
Astrocyte Inhibitory Glutamatergic 0.2
Oligodendrocyte Excitatory Glutamatergic 0.2
Inhibitory Inhibitory Dopaminergic 0.2
Excitatory Inhibitory 330 VLMC NN 0.2
Microglial Inhibitory Gabaergic 0.2
Endothelial Inhibitory 330 VLMC NN 0.2
Endothelial 0.2
Microglial Inhibitory Endothelial 0.1
330 VLMC NN 0.1
Endothelial Inhibitory Pericyte 0.1
Astrocyte Inhibitory Gabaergic 0.1
Pericyte Inhibitory 329 ABC NN 0.1
Oligodendrocyte Inhibitory 329 ABC NN 0.1
Pericyte Inhibitory Pericyte 0.1
OPC Inhibitory Glutamatergic 0.1
Neuroepithelial Inhibitory 330 VLMC NN 0.1
Excitatory Inhibitory Endothelial 0.1
Inhibitory Excitatory Glutamatergic 0.1
Inhibitory 330 VLMC NN 0.1
Microglial Inhibitory Oligodendrocyte 0.1
Astrocyte Inhibitory Oligodendrocyte 0.0
OPC 0.0
329 ABC NN 0.0
316 Bergmann NN 0.0
Astrocyte 0.0
330 VLMC NN 0.0
Endothelial 0.0
Excitatory Glutamatergic 0.0
Heterogeneous 0.0
Excitatory Inhibitory OPC 0.0
Dopaminergic 0.0
Endothelial Inhibitory 329 ABC NN 0.0
Microglial Inhibitory 329 ABC NN 0.0
Inhibitory Inhibitory Pericyte 0.0
329 ABC NN 0.0
Oligodendrocyte 0.0
Endothelial 0.0
Excitatory Inhibitory Microglia 0.0
Oligodendrocyte 0.0
329 ABC NN 0.0
Pericyte 0.0
Excitatory Gabaergic 0.0
330 VLMC NN 0.0
Inhibitory Inhibitory Inhibitory 0.0
OPC 0.0
Microglial Inhibitory Pericyte 0.0
Oligodendrocyte Inhibitory 316 Bergmann NN 0.0
Dopaminergic 0.0
OPC Inhibitory Pericyte 0.0
Endothelial 0.0
Neuroepithelial Inhibitory 325 CHOR NN 0.0
Microglial Excitatory Glutamatergic 0.0
OPC Inhibitory 316 Bergmann NN 0.0
Oligodendrocyte Inhibitory Astrocyte 0.0
Pericyte 0.0
OPC 0.0
325 CHOR NN 0.0
Endothelial 0.0
Excitatory Heterogeneous 0.0
Pericyte Inhibitory Gabaergic 0.0
Endothelial 0.0
Oligodendrocyte Oligodendrocyte Oligodendrocyte Oligodendrocyte 98.5
Glutamatergic 0.8
Heterogeneous 0.4
Gabaergic 0.1
Inhibitory Oligodendrocyte Oligodendrocyte 0.1
Oligodendrocyte Oligodendrocyte Endothelial 0.1
OPC Oligodendrocyte Oligodendrocyte 0.0
Excitatory Oligodendrocyte Heterogeneous 0.0
Oligodendrocyte 0.0
Glutamatergic 0.0
Astrocyte Oligodendrocyte Heterogeneous 0.0
Oligodendrocyte Oligodendrocyte 329 ABC NN 0.0
330 VLMC NN 0.0
Pericyte Oligodendrocyte Oligodendrocyte 0.0
Neuron Excitatory Excitatory Glutamatergic 57.6
Inhibitory Excitatory Gabaergic 29.2
Inhibitory Gabaergic 4.1
Excitatory Excitatory Heterogeneous 3.9
Inhibitory Inhibitory Heterogeneous 1.2
Neuroepithelial Excitatory 325 CHOR NN 0.9
Oligodendrocyte Excitatory Glutamatergic 0.8
Heterogeneous 0.5
OPC Excitatory Glutamatergic 0.3
Inhibitory Excitatory Glutamatergic 0.3
Excitatory Excitatory Gabaergic 0.2
Inhibitory Gabaergic 0.2
Oligodendrocyte Excitatory 325 CHOR NN 0.2
Inhibitory Excitatory Heterogeneous 0.2
Oligodendrocyte Inhibitory Gabaergic 0.1
Astrocyte Excitatory Glutamatergic 0.1
325 CHOR NN 0.1
Endothelial Excitatory 325 CHOR NN 0.1
Oligodendrocyte Excitatory Gabaergic 0.1
Excitatory Inhibitory Heterogeneous 0.1
Astrocyte Inhibitory Gabaergic 0.0
Inhibitory Excitatory Inhibitory 0.0
Astrocyte Excitatory Heterogeneous 0.0
Excitatory Excitatory 325 CHOR NN 0.0
Pericyte 0.0
Astrocyte Excitatory Gabaergic 0.0
Microglial Excitatory Glutamatergic 0.0
325 CHOR NN 0.0
Inhibitory Excitatory 329 ABC NN 0.0
Pericyte 0.0
325 CHOR NN 0.0
Oligodendrocyte Inhibitory Heterogeneous 0.0
Excitatory Oligodendrocyte 0.0
Pericyte Excitatory Glutamatergic 0.0
Astrocyte Astrocyte Astrocyte Astrocyte 87.9
Heterogeneous 6.7
316 Bergmann NN 2.0
Inhibitory Astrocyte Inhibitory 1.6
Oligodendrocyte Astrocyte Heterogeneous 0.5
Astrocyte Astrocyte Inhibitory 0.2
Endothelial 0.2
Excitatory Astrocyte Heterogeneous 0.2
Inhibitory 0.1
Astrocyte Astrocyte Pericyte 0.1
Oligodendrocyte Astrocyte 316 Bergmann NN 0.1
Astrocyte 0.1
Astrocyte Astrocyte Glutamatergic 0.0
330 VLMC NN 0.0
Gabaergic 0.0
Microglial Astrocyte 316 Bergmann NN 0.0
Inhibitory Astrocyte Astrocyte 0.0
Heterogeneous 0.0
OPC Astrocyte Astrocyte 0.0
Neuroepithelial Astrocyte Heterogeneous 0.0
Microglial Astrocyte Heterogeneous 0.0
Oligodendrocyte Astrocyte Inhibitory 0.0
Glutamatergic 0.0
Endothelial 0.0
Pericyte Astrocyte Inhibitory 0.0
Heterogeneous 0.0
Name: proportion, dtype: float64
OSD-612-Specific¶
Clean variable names & keys
self.rna.obs.loc[:, "Age_End"] = self.rna.obs[
"Characteristics[Age at Euthanasia]"].astype(str) + " Weeks"
self.rna.obs.loc[:, "Condition"] = self.rna.obs["Factor Value[Spaceflight]"]
if "cellmap_class_name" in self.rna.obs: # cell name w/o region cues
self.rna.obs.loc[:, "cellmap_class_name_collapsed"] = self.rna.obs[
"cellmap_class_name"].apply(lambda x: x + "***" + str(
"Glutamatergic-" if ("glut" in x.lower()) else "") + str(
"GABAergic-" if "gaba" in x.lower() else "") + str(
"Oligodendrocyte-" if "oligo" in x.lower() else "") + str(
"Cholinergic-" if "chol" in x.lower() else ""))
self.rna.obs.loc[:, "cellmap_class_name_collapsed"] = self.rna.obs[
"cellmap_class_name_collapsed"].apply(
lambda x: x.split("***")[0 if x.split("***")[1] == "" else 1])
self.rna.obs.loc[:, "cellmap_class_name_collapsed"] = self.rna.obs[
"cellmap_class_name_collapsed"].apply(
lambda x: x[:-1] if x[-1] == "-" else x)
Final Write¶
# Descriptives
if all((i in self.rna.obs for i in [
"Factor Value[Age]", "Characteristics[Age at Euthanasia]"])):
print(self.rna.obs[["Factor Value[Age]",
"Characteristics[Age at Euthanasia]"]].value_counts())
# Write h5ad
# self.rna.X = self.rna.layers["counts"].copy()
if overwrite is True or not os.path.exists(file_new):
print("\n\n", f"Writing file to {file_new}...")
self.rna.write_h5ad(file_new)
## Write Version Compatible with Older Packages
# adata = self.rna.copy()
# adata.uns = {}
# # adata.write_h5ad(os.path.splitext(file_new)[0] + "_compatible.h5ad")
# Send Email with Output When Done
if email is not None and html_out is not None:
os.system(f"jupyter nbconvert --to html {cur_file}")
os.system(f"echo 'yay' | mutt -s 'JOB DONE' -a {html_out} -- {email}")
... storing 'Age_End' as categorical
Writing file to data/OSD-612_integrated.h5ad...
SCRATCH (IGNORE)¶
# mks_c = dict(zip(markers_predefined, [markers_predefined[x].intersection(
# self.rna.var_names) for x in markers_predefined]))
# mks_c["Inhibitory"] = mks_c["Inhibitory"].difference(mks_c["Excitatory"])
# mks_c["Excitatory"] = mks_c["Excitatory"].difference(mks_c["Inhibitory"])
# endo = list(mks_c["Endothelial"].difference(set(functools.reduce(
# lambda i, j: i + j, [list(
# mks_c[i]) for i in mks_c if i != "Endothelial"]))))
# _ = self.plot(genes=endo, figsize=(15, 15),
# col_celltype="annotation_scanvi",
# # col_celltype="annotation_by_overlap",
# layer="scaled", standard_scale="var", kind="matrix")
if email is not None and html_out is not None:
os.system(f"jupyter nbconvert --to html {cur_file}")
os.system(f"echo 'yay' | mutt -s 'JOB DONE' -a {html_out} -- {email}")